Systems and methods for detecting glucose level data patterns

ABSTRACT

Systems and methods for detecting and reporting patterns in analyte concentration data are provided. According to some implementations, an implantable device for continuous measurement of an analyte concentration is disclosed. The implantable device includes a sensor configured to generate a signal indicative of a concentration of an analyte in a host, a memory configured to store data corresponding at least one of the generated signal and user information, a processor configured to receive data from at least one of the memory and the sensor, wherein the processor is configured to generate pattern data based on the received information, and an output module configured to output the generated pattern data. The pattern data can be based on detecting frequency and severity of analyte data in clinically risky ranges.

INCORPORATION BY REFERENCE TO RELATED APPLICATIONS

Any and all priority claims identified in the Application Data Sheet, orany correction thereto, are hereby incorporated by reference under 37CFR 1.57. This application is a continuation of U.S. application Ser.No. 13/566,844, filed Aug. 3, 2012, which is a continuation of U.S.application Ser. No. 13/566,678, filed Aug. 3, 2012, now U.S. Pat. No.10,349,871, which claims the benefit of U.S. Provisional Appl. No.61/515,786, filed Aug. 5, 2011, and U.S. Provisional Appl. No.61/660,650, filed Jun. 15, 2012. Each of the aforementioned applicationsis incorporated by reference herein in its entirety, and each is herebyexpressly made a part of this specification.

FIELD OF THE INVENTION

The embodiments relate generally to systems and methods for analyzingand detecting patterns in data received from an analyte sensor, such asa glucose sensor.

BACKGROUND OF THE INVENTION

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high blood sugar, which causes an arrayof physiological derangements (kidney failure, skin ulcers, or bleedinginto the vitreous of the eye) associated with the deterioration of smallblood vessels. A hypoglycemic reaction (low blood sugar) may be inducedby an inadvertent overdose of insulin, or after a normal dose of insulinor glucose-lowering agent accompanied by extraordinary exercise orinsufficient food intake.

Conventionally, a diabetic person carries a self-monitoring bloodglucose (SMBG) monitor, which typically requires uncomfortable fingerpricking methods. Due to the lack of comfort and convenience, a diabeticwill normally only measure his or her glucose level two to four timesper day. Unfortunately, these time intervals are spread so far apartthat the diabetic will likely find out too late, sometimes incurringdangerous side effects, of a hyperglycemic or hypoglycemic condition. Infact, it is not only unlikely that a diabetic will take a timely SMBGvalue, but additionally the diabetic will not know if his blood glucosevalue is going up (higher) or down (lower) based on conventionalmethods.

Consequently, a variety of non-invasive, transdermal (e.g.,transcutaneous) and/or implantable electrochemical sensors are beingdeveloped for continuously detecting and/or quantifying blood glucosevalues. These devices generally transmit raw or minimally processed datafor subsequent analysis by the device or at a remote device, which caninclude a display.

Conventional processing of the raw data by a device are generallydirected towards displaying information to the users regarding theirrecent glucose trend and helping them take short term actions, which inturn helps them stay in the target range and improves the averageglucose over a period of time. Patients may also review data downloadseither on their own or with their health care physician to decide onlonger term behavioral changes.

However, some issues with conventional tools for analyzing data exist.Among these issues are the amount of time required to analyze the dataand the lack of user participation in downloading or analyzing the data.For example, reviewing the downloads using trend graphs is timeconsuming and requires some amount of expertise to detect problem areas.Additionally, many users do not consider reviewing downloads, or eveninitiating downloads from the receivers, and are often unaware of someissues that may exist. Finally, conventional techniques may provideexcessive alerts to the user, including alerts in response tomeasurements that do not pose a risk to the user. As a result, the usermay ignore some important alerts which are provided by the conventionalsystems to their detriment.

SUMMARY OF THE INVENTION

Details of one or more implementations of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages will becomeapparent from the description, the drawings, and the claims. Note thatthe relative dimensions of the following figures may not be drawn toscale.

Accordingly, in a first aspect, an analyte concentration monitoringsystem is provided, comprising: a display screen; one or moreprocessors; an input module configured to receive sensor data from ananalyte sensor configured to generate sensor data points indicative of ameasured an analyte concentration; memory; and one or more programs,wherein the one or more programs are stored in the memory and areconfigured to be executed by the one or more processors, the one or moreprograms including: instructions to apply a weighted value to eachsensor data point falling within a timeframe of sensor data, theweighted value depending upon a concentration value of the sensor data;instructions to aggregate the weighted sensor data according to time ofday; and instructions to display the aggregated, weighted sensor data ona chart.

In an embodiment of the first aspect or in combination with any otherone or more embodiments of the first aspect, aggregating the sensor dataaccording to time of day includes aggregating each sensor data pointthat falls within the same five minute interval of a day.

In an embodiment of the first aspect or in combination with any otherone or more embodiments of the first aspect, the system furthercomprises instructions to highlight significant time ranges of thedisplayed sensor data, wherein significant time ranges of displayed dataare determined based on exceeding thresholds for both frequency andseverity.

In an embodiment of the first aspect or in combination with any otherone or more embodiments of the first aspect, the determination ofsignificant time ranges has an increased sensitivity during a predefinednighttime range of time, wherein increased sensitivity corresponds tolower frequency and severity thresholds.

In an embodiment of the first aspect or in combination with any otherone or more embodiments of the first aspect, displaying the aggregated,weighted data includes displaying a high pattern chart and a low patternchart, wherein sensor data associated with the high pattern chart areweighted differently than sensor data displayed in the low patternchart.

In an embodiment of the first aspect or in combination with any otherone or more embodiments of the first aspect, the chart includes anx-axis indicative of the timeframe and a y-axis indicative of amagnitude, wherein the y-axis scale is a predetermined percentage thatless than a 100% of the maximum possible sum of weighted values.

In an embodiment of the first aspect or in combination with any otherone or more embodiments of the first aspect, the system furthercomprises instructions to display a pattern summary table on thedisplay, the pattern summary table indicating a total number ofsignificant pattern matches and a description of one or more of the mostsignificant matches, wherein the pattern matches are grouped into aplurality of categories corresponding to time of day and glucose level,wherein the most significant pattern match falling within in each of thefour groups can is determined by based on a total sum of all contributedvalues within the pattern match interval.

In an embodiment of the first aspect or in combination with any otherone or more embodiments of the first aspect, the system furthercomprises a user interface incorporating the display screen, wherein theuser interface includes a timeframe selection control that allows a userto select of modify the timeframe.

In an embodiment of the first aspect or in combination with any otherone or more embodiments of the first aspect, the timeframe selectioncontrol includes a user selectable drop down menu configured to allow auser to select or modify a number of days of the timeframe.

In an embodiment of the first aspect or in combination with any otherone or more embodiments of the first aspect, the timeframe selectioncontrol includes a slider bar that a user can drag horizontally tomodify the start and end dates of the timeframe.

In an embodiment of the first aspect or in combination with any otherone or more embodiments of the first aspect, the chart is automaticallyupdated based on modification of the timeframe using the timeframeselection control.

In a second aspect, a computer-implemented method is provided foridentifying patterns in continuous analyte data, the method comprising:obtaining analyte data points from computer memory falling within adesignated date range; applying one of more filters to the analyte datato generate contributor data points; weighting each the contributor datapoint based on the contributor data point's analyte value; assigningeach weighted contributor data point to a matching epoch; identifying ifan epoch is a match based on whether the contributor or contributorsassigned to the epoch meet at least one pattern threshold; determiningone or more patterns by scanning the epochs for flags; and outputtinginformation representative of the determined one or more patterns.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, the designated date rangeis at least three days.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, the one or more filterscomprises filters selected from the group consisting of: (i) an analytevalue filter that filters out analyte data points that have an analyteconcentration value falling outside of a predetermined analyte level orrange of analyte values; (ii) a time range filter that filters outanalyte data points measurements that falls outside of a time of dayrange; (iii) a day of the week filter that filters out analyte datapoints measured one or more predetermined days of the week; and (iv) anevent filter that filters out data points that do not fall within apredetermined amount of time from an occurrence of an event.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, the event is an eventselected from the group consisting of exercise, a meal, sleep andadministration of a medication.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, the method furthercomprises receiving user input indicative of the event using a userinterface.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, weighting each contributordata point is based on a predetermined weight assignment map ormathematical assignment function.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, weighting each contributorcomprises assigning a weighted value to each contributor, wherein theassigned weighted value is smaller if the analyte value is lessclinically significant than if the analyte value is more clinicallysignificant.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, the assignment of weightedvalues is non-linear based on the analyte value.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, each epoch spans adetermined time of day and wherein each contributor is added to theepoch that spans the corresponding time of day.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, the pattern thresholdscomprise thresholds selected from the group consisting of: a thresholdminimum number of contributors in an epoch; a threshold average weightedvalue of the contributors in an epoch; a threshold medium weighted valueof the contributors in the epoch; a threshold sum of the weighted valuesof the contributors in the epoch; a threshold average difference of theweighted values of the contributors in the epoch; a threshold standarddeviation value of the weighted values of the contributors in the epoch;and a threshold correlation value.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, at least one of the one ormore pattern thresholds is defined in terms of a percentage.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, an epoch is flagged whenat least some of the one or more thresholds are satisfied.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, determining the one ormore patterns comprises determining a start and an end of the pattern.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, determining the start andthe end of the pattern identifying a predetermined minimum number ofcontiguous matching epochs or a predetermined ratio of contiguousmatching to non-matching epochs.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, the start of the patternis determined based on identifying a first threshold number ofcontiguous matching epochs and the end of the pattern is determinedbased on identifying a second threshold number non-matching epochs.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, the outputted informationcomprises the start time and the end time.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, determining the one ormore patterns is based on the frequency of matching epochs and theweighted values of the matching epochs.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, wherein the outputtedinformation comprises a triggering alert notifying a user of the one ormore detected patterns.

In an embodiment of the second aspect or in combination with any otherone or more embodiments of the second aspect, the outputted informationis processed further to form or modify a medication administrationroutine.

In a third aspect, an analyte concentration pattern detection system isprovided which is configured to perform the method of the second aspector any one or more of its associated embodiments, the system comprisinga continuous analyte sensor to generate the analyte concentration datapoints, computer memory to store the generated analyte data points, aprocessor module configured to perform the applying, the weighting, theassigning, the identifying, and the determining.

In an embodiment of the third aspect, the system further comprisesinstructions stored in the memory, wherein the instructions, whenexecuted by the processor module, cause the processor module to performthe applying, the weighting, the assigning, the identifying, and thedetermining.

In a fourth aspect, an analyte monitoring system is provided which isconfigured to measure an analyte concentration of a host, the systemcomprising: a sensor configured to generate sensor data indicative of aconcentration of an analyte in a host over time; a memory configured tostore the sensor data; a processor configured to receive the sensor datafrom at least one of the memory and detecting a pattern in the data, thedetecting comprising identifying a plurality of events based on thesensor data, associating at least some of the plurality of events basedon a criterion to from a set of events, and qualifying the set of eventsas the detected pattern; and an output module configured to outputinformation representative of the detected pattern.

In an embodiment of the fourth aspect or in combination with any otherone or more embodiments of the fourth aspect, the criterion is atimeframe.

In an embodiment of the fourth aspect or in combination with any otherone or more embodiments of the fourth aspect, qualifying the set ofevents as a pattern comprises determining a priority score associatedwith the set.

In an embodiment of the fourth aspect or in combination with any otherone or more embodiments of the fourth aspect, the set qualifies as apattern if the priority score exceeds a threshold.

In an embodiment of the fourth aspect or in combination with any otherone or more embodiments of the fourth aspect, the associating includesforming a plurality of sets, and wherein the set qualifies as a patternif the priority score of the set is greater than a predetermined numberof priority scores associated with the plurality of sets.

In an embodiment of the fourth aspect or in combination with any otherone or more embodiments of the fourth aspect, the qualifying furthercomprises scoring each event in a set based on one or more scoringcriteria, wherein the priority score is a summation of the scoresassociated with the events in the set.

In an embodiment of the fourth aspect or in combination with any otherone or more embodiments of the fourth aspect, the scoring criteriacomprising a time of day associated with the event, wherein eventsassociated with certain predefined times of day are scored higher thanevents associated with other times of day.

In an embodiment of the fourth aspect or in combination with any otherone or more embodiments of the fourth aspect, identifying the pluralityof events comprises calculating an average distance for a segment oftime of the sensor data that exceeds a first predetermined thresholdanalyte level.

In an embodiment of the fourth aspect or in combination with any otherone or more embodiments of the fourth aspect, identifying the pluralityof events further comprises comparing a total amount of time the segmentexceeds a second predetermined threshold analyte level to a thresholdamount of time.

In an embodiment of the fourth aspect or in combination with any otherone or more embodiments of the fourth aspect, the first predeterminedthreshold analyte level and the second predetermined threshold analytelevel are the same.

In an embodiment of the fourth aspect or in combination with any otherone or more embodiments of the fourth aspect, the outputting comprisesdisplaying the information representative of the detected pattern on auser interface, wherein the displaying comprises one or more ofdisplaying a line graph depicting the detected pattern, highlighting achart corresponding to the detected pattern and providing a timeframe ina textual format of a timeframe associated with the detected pattern.

In an embodiment of the fourth aspect or in combination with any otherone or more embodiments of the fourth aspect, the system furthercomprises instructions stored in computer memory, wherein theinstructions, when executed by the processor, cause the processor toperform the detecting and outputting.

In a fifth aspect, a method is provided for identifying patterns basedon monitored analyte concentration sensor data, the method comprising:receiving data from at least one input, the data including measurementsof an analyte concentration and time of day information associated withthe measurement; analyzing the received data to identify a plurality ofclinically significant events; determining patterns in the analyzeddata, the determining comprising grouping the events based on time ofday information into a plurality of event sets; and displayinginformation based on one or more of the determined patterns.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, the measurements aregenerated using a continuous analyte sensor.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, the method furthercomprises receiving user input indicating a timeframe and selecting themeasurements that fall within the timeframe for the analyzing.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, the analyzing comprisesdetecting a plurality of episodes, wherein each of the plurality ofepisodes is detected by scanning the measurements using predefinedcriteria to determine a start and end of each episode.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, analyzing further comprisesqualifying an episode as an event by filtering the episodes based on oneor more episode characteristics.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, the characteristicscomprise one or more of an average distance below a predeterminedanalyte level and a total time below a predetermined analyte level.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, grouping the eventscomprises calculating times between the events and grouping the eventsinto sets based on the times.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, calculating the timesbetween the events comprises calculating a time between the end of afirst event and the end of a second event.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, calculating the timesbetween the events comprises calculating a time between a nadir point ofa first event and a nadir point of a second event.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, the determining furthercomprises selecting one or more sets as patterns, the selectingcomprising filtering the plurality of sets based on a priority score ofeach group.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, the filtering filters outsets that have a priority score less than a threshold amount.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, the filtering filters outsets that have a priority score that is lower than the priority score ofa predetermined number of other sets of the plurality of sets.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, the measurements areglucose concentration measurements and the events are hypoglycemicevents.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, the displaying comprisesdisplaying a timeframe corresponding to the at least one or moredetected patterns.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, the method is performedautomatically upon the expiration of a predetermined amount of time.

In an embodiment of the fifth aspect or in combination with any otherone or more embodiments of the fifth aspect, the method is performedresponsive to user input indicative of a request to initiate patterndetection.

In a sixth aspect, an analyte concentration pattern detection system isprovided which is configured to perform the method according to thefifth aspect or any one or more of its associated embodiments, thesystem comprising a continuous analyte sensor to generate the analyteconcentration measurements, computer memory to store the generatedanalyte concentration measurements, a processor module configured toperform the analyzing and determining, and a user interface configuredto perform the displaying.

In an embodiment of the sixth aspect, the system further comprisesinstructions stored in the memory, wherein the instructions, whenexecuted by the processor module, cause the processor module to performthe analyzing and determining.

In a seventh aspect, a method is provided for alerting a user based onmeasurements of an analyte concentration, the method comprising:receiving data from at least one input, the data including measurementsof an analyte concentration and time of day information; analyzing thereceived data; detecting a hypoglycemic event based on the dataexceeding at least one predetermined threshold; and displaying a trendgraph of glucose measurements over time on a user interface, wherein thetrend graph includes an indication of a hypoglycemic reoccurrence riskassociated with a predetermined amount of time following the detectedhypoglycemic event.

In an embodiment of the seventh aspect or in combination with any otherone or more embodiments of the seventh aspect, detecting thehypoglycemia event comprises determining an average distance and a timea segment of time the measured analyte concentration is below apredetermined analyte concentration level.

In an embodiment of the seventh aspect or in combination with any otherone or more embodiments of the seventh aspect, the method of Claim C1 isperformed only when a user-selectable setting is turned on.

In an embodiment of the seventh aspect or in combination with any otherone or more embodiments of the seventh aspect, the predetermined amountof time is 48 hours.

In an embodiment of the seventh aspect or in combination with any otherone or more embodiments of the seventh aspect, the method furthercomprises triggering an audible or visual alert using the user interfaceresponsive to detecting the hypoglycemic event and detecting that acurrent rate of change of the analyte concentration exceeds apredetermined threshold.

In an embodiment of the seventh aspect or in combination with any otherone or more embodiments of the seventh aspect, the method furthercomprises triggering an audible or visual alert using the user interfaceresponsive to detecting the hypoglycemic event and detecting that thecurrent analyte concentration is below a predetermined threshold.

In an eighth aspect, an analyte concentration pattern detection systemis provided which is configured to perform the method according to theseventh aspect or any one or more of its associated embodiments, thesystem comprising a continuous analyte sensor to generate the analyteconcentration measurements, computer memory to store the generatedanalyte concentration measurements, a processor module configured toperform the detecting, and a user interface configured to perform thedisplaying.

In an embodiment of the eighth aspect, the system further comprisesinstructions stored in the memory, wherein the instructions, whenexecuted by the processor module, cause the processor module to performthe detecting.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating one embodiment of a continuous analytesensor system including a sensor electronics module.

FIG. 2A is a perspective view of a sensor system including a mountingunit and sensor electronics module attached thereto according to oneembodiment.

FIG. 2B is a side view of the sensor system of FIG. 2B.

FIG. 3 is an exemplary block diagram illustrating various elements ofone embodiment of a continuous analyte sensor system and display device.

FIG. 4 illustrates a flowchart of a method of detecting patternsaccording to some embodiments.

FIGS. 5A-5E illustrate example graphs of glucose levels over a timeperiod according to some embodiments.

FIG. 6 illustrates a flowchart of a pattern detection method accordingto some embodiments.

FIG. 7 illustrates a flowchart of a pattern detection method accordingto some embodiments.

FIGS. 8A-8D illustrate plots of weighted assignment maps according tosome embodiments.

FIG. 9 illustrates another example of a weighted mapping of glucosevalues according to some embodiments.

FIG. 10 illustrates an example of a user interface for displayingpattern results according to some embodiments.

FIG. 11 illustrates another graphical user interface for displayingpattern results according to some embodiments.

FIG. 12 relates to a date slider control for selecting a date timeframeaccording to some embodiments

DETAILED DESCRIPTION OF THE EMBODIMENTS

The following description and examples illustrate some exemplaryembodiments of the disclosed invention in detail. Those of skill in theart will recognize that there are numerous variations and modificationsof this invention that are encompassed by its scope. Accordingly, thedescription of a certain exemplary embodiment should not be deemed tolimit the scope of the present invention.

Definitions

In order to facilitate an understanding of the systems and methodsdiscussed herein, a number of terms are defined below. The terms definedbelow, as well as other terms used herein, should be construed toinclude the provided definitions, the ordinary and customary meaning ofthe terms, and any other implied meaning for the respective terms. Thus,the definitions below do not limit the meaning of these terms, but onlyprovide exemplary definitions.

The term “analyte” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a substance or chemicalconstituent in a biological fluid (for example, blood, interstitialfluid, cerebral spinal fluid, lymph fluid or urine) that can beanalyzed. Analytes can include naturally occurring substances,artificial substances, metabolites, and/or reaction products. In someembodiments, the analyte for measurement by the sensor heads, devices,and methods is analyte. However, other analytes are contemplated aswell, including but not limited to acarboxyprothrombin; acylcarnitine;adenine phosphoribosyl transferase; adenosine deaminase; albumin;alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle),histidine/urocanic acid, homocysteine, phenylalanine/tyrosine,tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers;arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactiveprotein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholicacid; chloroquine; cholesterol; cholinesterase; conjugated 1-13hydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MMisoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine;dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcoholdehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Beckermuscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobin A,hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F,D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1,Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax,sexual differentiation, 21-deoxycortisol); desbutylhalofantrine;dihydropteridine reductase; diptheria/tetanus antitoxin; erythrocytearginase; erythrocyte protoporphyrin; esterase D; fattyacids/acylglycines; free β-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I;17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszky's disease virus, dengue virus, Dracunculusmedinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus,Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacterium leprae,Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenzavirus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa,respiratory syncytial virus, Rickettsia (scrub typhus), Schistosomamansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferring;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins, and hormones naturally occurring in blood or interstitialfluids can also constitute analytes in certain embodiments. The analytecan be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte can be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbituates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body can also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and5-Hydroxyindoleacetic acid (FHIAA).

The term “A/D Converter” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to hardware and/orsoftware that converts analog electrical signals into correspondingdigital signals.

The terms “processor module,” “microprocessor” and “processor” as usedherein are broad terms and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and are not to belimited to a special or customized meaning), and furthermore referwithout limitation to a computer system, state machine, and the likethat performs arithmetic and logic operations using logic circuitry thatresponds to and processes the basic instructions that drive a computer.

The terms “sensor data”, as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and are not to be limited to a special or customizedmeaning), and furthermore refers without limitation to any dataassociated with a sensor, such as a continuous analyte sensor. Sensordata includes a raw data stream, or simply data stream, of analog ordigital signal directly related to a measured analyte from an analytesensor (or other signal received from another sensor), as well ascalibrated and/or filtered raw data. In one example, the sensor datacomprises digital data in “counts” converted by an A/D converter from ananalog signal (e.g., voltage or amps) and includes one or more datapoints representative of a glucose concentration. Thus, the terms“sensor data point” and “data point” refer generally to a digitalrepresentation of sensor data at a particular time. The term broadlyencompasses a plurality of time spaced data points from a sensor, suchas a from a substantially continuous glucose sensor, which comprisesindividual measurements taken at time intervals ranging from fractionsof a second up to, e.g., 1, 2, or 5 minutes or longer. In anotherexample, the sensor data includes an integrated digital valuerepresentative of one or more data points averaged over a time period.Sensor data may include calibrated data, smoothed data, filtered data,transformed data, and/or any other data associated with a sensor.

The term “calibration” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a process of determining arelationship between a raw data stream and corresponding reference data,which can be used to convert raw data into calibrated data (definedbelow). In some embodiments, such as continuous analyte sensors, forexample, calibration can be updated or recalibrated over time as changesin the relationship between the raw data and reference data occur, forexample, due to changes in sensitivity, baseline, transport, metabolism,and the like.

The terms “calibrated data” and “calibrated data stream” as used hereinare broad terms and are to be given their ordinary and customary meaningto a person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto data that has been transformed from its raw state to another stateusing a function, for example a conversion function, to provide ameaningful value to a user.

The terms “smoothed data” and “filtered data” as used herein are broadterms and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto data that has been modified to make it smoother and more continuousand/or to remove or diminish outlying points, for example, by performinga moving average of the raw data stream. Examples of data filtersinclude FIR (finite impulse response), IIR (infinite impulse response),moving average filters, and the like.

The terms “smoothing” and “filtering” as used herein are broad terms andare to be given their ordinary and customary meaning to a person ofordinary skill in the art (and are not to be limited to a special orcustomized meaning), and furthermore refer without limitation to amathematical computation that attenuates or normalizes components of asignal, such as reducing noise errors in a raw data stream. In someembodiments, smoothing refers to modification of a data stream to makeit smoother and more continuous or to remove or diminish outlying datapoints, for example, by performing a moving average of the raw datastream.

The term “noise signal” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a signalassociated with noise on the data stream (e.g., non-analyte relatedsignal). The noise signal can be determined by filtering and/oraveraging, for example. In some embodiments, the noise signal is asignal residual, delta residual (difference of residual), absolute deltaresidual, and/or the like, which are described in more detail elsewhereherein.

The term “algorithm” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a computational process(associated with computer programming or other written instructions)involved in transforming information from one state to another.

The term “matched data pairs” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to reference data(for example, one or more reference analyte data points) matched withsubstantially time corresponding sensor data (for example, one or moresensor data points).

The term “counts” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a unit of measurement of adigital signal. In one example, a raw data stream measured in counts isdirectly related to a voltage (e.g., converted by an A/D converter),which is directly related to current from the working electrode. Inanother example, counter electrode voltage measured in counts isdirectly related to a voltage.

The term “sensor” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to any device (or portion of adevice) that measures a physical quantity and converts it into a signalthat can be processed by analog and/or digital circuitry. Thus, theoutput of a sensor may be an analog and/or digital signal. Examples ofsensors include analyte sensors, glucose sensors, temperature sensors,altitude sensors, accelerometers, and heart rate sensors.

The terms “glucose sensor” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and are not to be limited to a special or customizedmeaning), and furthermore refer without limitation to any sensor bywhich glucose can be quantified (e.g., enzymatic or non-enzymatic). Forexample, some embodiments of a glucose sensor may utilize a membranethat contains glucose oxidase that catalyzes the conversion of oxygenand glucose to hydrogen peroxide and gluconate, as illustrated by thefollowing chemical reaction:Glucose+O₂→Gluconate+H₂O₂

Because for each glucose molecule metabolized, there is a proportionalchange in the co-reactant O₂ and the product H₂O₂, one can use anelectrode to monitor the current change in either the co-reactant or theproduct to determine glucose concentration.

The terms “coupled”, “operably connected” and “operably linked” as usedherein are broad terms and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and are not to belimited to a special or customized meaning), and furthermore referwithout limitation to one or more components being linked to anothercomponent(s), either directly or indirectly, in a manner that allowstransmission of signals between the components. For example, modules ofa computing device that communicate via a common data bus are coupled toone another. As another example, one or more electrodes of a glucosesensor can be used to detect the amount of glucose in a sample andconvert that information into a signal, e.g., an electrical orelectromagnetic signal; the signal can then be transmitted to anelectronic circuit. In this case, the electrode is “operably linked” tothe electronic circuitry, even though the analog signal from theelectrode is transmitted and/or transformed by analog and/or digitalcircuitry before reaching the electronic circuit. These terms are broadenough to include wireless connectivity.

The term “physically connected” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and are not to be limited to a special or customizedmeaning), and furthermore refers without limitation to one or morecomponents that are connected to another component(s) through directcontact and/or a wired connection, including connecting via one or moreintermediate physically connecting component(s). For example, a glucosesensor may be physically connected to a sensor electronics module, andthus the processor module located therein, either directly or via one ormore electrical connections.

The term “substantially” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to being largely butnot necessarily wholly that which is specified.

The term “host” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to mammal, such as a humanimplanted with a device.

The term “continuous analyte sensor” as used herein is a broad term andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to adevice, or portion of a device, that continuously or continuallymeasures a concentration of an analyte, for example, at time intervalsranging from fractions of a second up to, for example, 1, 2, or 5minutes, or longer. In one exemplary embodiment, a glucose sensorcomprises a continuous analyte sensor, such as is described in U.S. Pat.No. 7,310,544, which is incorporated herein by reference in itsentirety.

The term “continuous analyte sensing” as used herein is a broad term andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to theperiod in which monitoring of an analyte is continuously or continuallyperformed, for example, at time intervals ranging from fractions of asecond up to, for example, 1, 2, or 5 minutes, or longer. In oneembodiment, a glucose sensor performs continuous analyte sensing inorder to monitor a glucose level in a corresponding host.

The terms “reference analyte monitor,” “reference analyte meter,” and“reference analyte sensor” as used herein are broad terms and are to begiven their ordinary and customary meaning to a person of ordinary skillin the art (and are not to be limited to a special or customizedmeaning), and furthermore refer without limitation to a device thatmeasures a concentration of an analyte and can be used as a referencefor a continuous analyte sensor, for example a self-monitoring bloodglucose meter (SMBG) can be used as a reference for a continuous glucosesensor for comparison, calibration, and the like.

The term “clinical acceptability”, as used herein, is a broad term andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and refers without limitation to determination ofthe risk of inaccuracies to a patient. Clinical acceptability mayconsider a deviation between time corresponding glucose measurements(e.g., data from a glucose sensor and data from a reference glucosemonitor) and the risk (e.g., to the decision making of a diabeticpatient) associated with that deviation based on the glucose valueindicated by the sensor and/or reference data. One example of clinicalacceptability may be 85% of a given set of measured analyte valueswithin the “A” and “B” region of a standard Clarke Error Grid when thesensor measurements are compared to a standard reference measurement.

The term “quality of calibration” as used herein, is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the statistical associationof matched data pairs in the calibration set used to create theconversion function. For example, an R-value may be calculated for acalibration set to determine its statistical data association, whereinan R-value greater than 0.79 determines a statistically acceptablecalibration quality, while an R-value less than 0.79 determinesstatistically unacceptable calibration quality.

The term “sensor session” as used herein, is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to a period of time a sensor isin use, such as but not limited to a period of time starting at the timethe sensor is implanted (e.g., by the host) to removal of the sensor(e.g., removal of the sensor from the host's body and/or removal of thesensor electronics module from the sensor housing).

The terms “noise,” “noise event(s),” “noise episode(s),” “signalartifact(s),” “signal artifact event(s),” and “signal artifactepisode(s)” as used herein are broad terms and are to be given theirordinary and customary meaning to a person of ordinary skill in the art(and are not to be limited to a special or customized meaning), andfurthermore refer without limitation to signal noise that issubstantially non-glucose related, such as interfering species, macro-or micro-motion, ischemia, pH changes, temperature changes, pressure,stress, or even unknown sources of mechanical, electrical and/orbiochemical noise for example.

The term “measured analyte values” as used herein is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an analyte valueor set of analyte values for a time period for which analyte data hasbeen measured by an analyte sensor. The term is broad enough to includesensor data from the analyte sensor before or after data processing inthe sensor and/or receiver (for example, data smoothing, calibration,and the like).

The term “estimated analyte values” as used herein is a broad term andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to ananalyte value or set of analyte values, which have been algorithmicallyextrapolated from measured analyte values. In some embodiments,estimated analyte values are estimated for a time period during which nodata exists. However, estimated analyte values can also be estimatedduring a time period for which measured data exists, but is to bereplaced by algorithmically extrapolated (e.g. processed or filtered)data due to noise or a time lag in the measured data, for example.

The term “calibration information” as used herein is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to any informationuseful in calibration of a sensor. Calibration information may includereference data received from a reference analyte monitor, including oneor more reference data points, one or more matched data pairs formed bymatching reference data (e.g., one or more reference glucose datapoints) with substantially time corresponding sensor data (e.g., one ormore continuous sensor data points), a calibration set formed from a setof one or more matched data pairs, a calibration line drawn from thecalibration set, in vitro parameters (e.g., sensor sensitivity), and/ora manufacturing code, for example.

The term “alarm” as used herein is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to an alert or signal, such as anaudible, visual, or tactile signal, triggered in response to one or morealarm conditions. In one embodiment, hyperglycemic and hypoglycemicalarms are triggered when present or predicted clinical danger isassessed based on continuous analyte data.

The term “transformed sensor data” as used herein is a broad term, andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to anydata that is derived, either fully or in part, from raw sensor data fromone or more sensors. For example, raw sensor data over a time period(e.g., 5 minutes) may be processed in order to generated transformedsensor data including one or more trend indicators (e.g., a 5 minutetrend). Other examples of transformed data include filtered sensor data(e.g., one or more filtered analyte concentration values), calibratedsensor data (e.g., one or more calibrated analyte concentration values),rate of change information, trend information, rate of accelerationinformation, sensor diagnostic information, location information,alarm/alert information, calibration information, and/or the like.

The term “sensor information” as used herein is a broad term, and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to informationassociated with measurement, signal processing (including calibration),alarms, data transmission, and/or display associated with a sensor, suchas a continuous analyte sensor. The term is broad enough to include rawsensor data (one or more raw analyte concentration values), as well astransformed sensor data. In some embodiments, sensor informationincludes displayable sensor information.

The term “displayable sensor information” as used herein is a broadterm, and is to be given its ordinary and customary meaning to a personof ordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation toinformation that is transmitted for display on one or more displaydevices. As is discussed elsewhere herein, the content of displayablesensor information that is transmitted to a particular display devicemay be customized for the particular display device. Additionally,formatting of displayable sensor information may be customized forrespective display devices. Displayable sensor information may includeany sensor data, including raw sensor data, transformed sensor data,and/or any information associated with measurement, signal processing(including calibration), and/or alerts associated with one or moresensors.

The term “data package” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a combination ofdata that is transmitted to one or more display devices, such as inresponse to triggering of an alert. A data package may includedisplayable sensor information (e.g., that has been selected andformatted for a particular display device) as well as headerinformation, such as data indicating a delivery address, communicationprotocol, etc. Depending on the embodiment, a data package may comprisesmultiple packets of data that are separately transmitted to a displaydevice (and reassembled at the display device) or a single block of datathat is transmitted to the display device. Data packages may beformatted for transmission via any suitable communication protocol,including radio frequency, Bluetooth, universal serial bus, any of thewireless local area network (WLAN) communication standards, includingthe IEEE 802.11, 802.15, 802.20, 802.22 and other 802 communicationprotocols, and/or a proprietary communication protocol.

The term “direct wireless communication” as used herein is a broad term,and is to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to a datatransmission that goes from one device to another device without anyintermediate data processing (e.g., data manipulation). For example,direct wireless communication between a sensor electronics module and adisplay device occurs when the sensor information transmitted from thesensor electronics module is received by the display device withoutintermediate processing of the sensor information. The term is broadenough to include wireless communication that is transmitted through arouter, a repeater, a telemetry receiver (e.g., configured tore-transmit the sensor information without additional algorithmicprocessing), and the like. The term is also broad enough to includetransformation of data format (e.g., via a Bluetooth receiver) withoutsubstantive transformation of the sensor information itself.

The term “prospective algorithm(s)” as used herein is a broad term, andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation toalgorithms that process sensor information in real-time (e.g.,continuously and/or periodically as sensor data is received from thecontinuous analyte sensor) and provide real-time data output (e.g.,continuously and/or periodically as sensor data is processed in thesensor electronics module).

The term “retrospective algorithm(s)” as used herein is a broad term,and is to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation toalgorithms that process sensor information in retrospect, (e.g.,analysis of a set of data for a time period previous to the present timeperiod).

As employed herein, the following abbreviations apply: Eq and Eqs(equivalents); mEq (milliequivalents); M (molar); mM (millimolar) μM(micromolar); N (Normal); mol (moles); mmol (millimoles); μmol(micromoles); nmol (nanomoles); g (grams); mg (milligrams); μg(micrograms); Kg (kilograms); L (liters); mL (milliliters); dL(deciliters); μL (microliters); cm (centimeters); mm (millimeters); μm(micrometers); nm (nanometers); h and hr (hours); min. (minutes); s andsec. (seconds); ° C. (degrees Centigrade).

Overview

In some embodiments, a system is provided for continuous measurement ofan analyte in a host that includes: a continuous analyte sensorconfigured to continuously measure a concentration of the analyte in thehost and a sensor electronics module physically connected to thecontinuous analyte sensor during sensor use. In one embodiment, thesensor electronics module includes electronics configured to process adata stream associated with an analyte concentration measured by thecontinuous analyte sensor in order to generate displayable sensorinformation that includes raw sensor data, transformed sensor data,and/or any other sensor data, for example. The sensor electronics modulemay further be configured to generate displayable sensor informationthat is customized for respective display devices, such that differentdisplay devices may receive different displayable sensor information.

Alerts

In some advantageous embodiments, one or more alerts are associated witha sensor electronics module. For example, alerts may be defined by oneor more alert conditions that indicate when a respective alert should betriggered. For example, a hypoglycemic alert may include alertconditions indicating a glucose level below a threshold. The alertconditions may also be based on transformed or analyzed sensor data,such as trending data, pattern data, and/or sensor data from multipledifferent sensors (e.g. an alert may be based on sensor data from both aglucose sensor and a temperature sensor). For example, a hypoglycemicalert may include alert conditions indicating a minimum required trendin the host's glucose level that must be present before triggering thealert. The term “trend,” as used herein refers generally to dataindicating some attribute of data that is acquired over time, e.g., suchas calibrated or filtered data from a continuous glucose sensor. A trendmay indicate amplitude, rate of change, acceleration, direction, etc.,of data, such as sensor data, including transformed or raw sensor data.

In one embodiment, each of the alerts is associated with one or moreactions that are to be performed in response to triggering of the alert.Alert actions may include, for example, activating an alarm, such asdisplaying information on a display of the sensor electronics module oractivating an audible or vibratory alarm coupled to the sensorelectronics module, and/or transmitting data to one or more displaydevices external to the sensor electronics module. For any deliveryaction that is associated with a triggered alert, one or more deliveryoptions define the content and/or format of the data to be transmitted,the device to which the data is to be transmitted, when the data is tobe transmitted, and/or a communication protocol for delivery of thedata.

In one embodiment, multiple delivery actions (each having respectivedelivery options) may be associated with a single alert such thatdisplayable sensor information having different content and formatting,for example, is transmitted to respective display devices in response totriggering of a single alert. For example, a mobile telephone mayreceive a data package including minimal displayable sensor information(that may be formatted specifically for display on the mobiletelephone), while a desktop computer may receive a data packageincluding most (or all) of the displayable sensor information that isgenerated by the sensor electronics module in response to triggering ofa common alert. Advantageously, the sensor electronics module is nottied to a single display device, rather it is configured to communicatewith a plurality of different display devices directly, systematically,simultaneously (e.g., via broadcasting), regularly, periodically,randomly, on-demand, in response to a query, based on alerts or alarms,and/or the like.

In some embodiments, clinical risk alerts are provided that includealert conditions that combine intelligent and dynamic estimativealgorithms that estimate present or predicted danger with greateraccuracy, more timeliness in pending danger, avoidance of false alarms,and less annoyance for the patient. In general, clinical risk alertsinclude dynamic and intelligent estimative algorithms based on analytevalue, rate of change, acceleration, clinical risk, statisticalprobabilities, known physiological constraints, and/or individualphysiological patterns, thereby providing more appropriate, clinicallysafe, and patient-friendly alarms. Co-pending U.S. Publ. No.2007-0208246-A1, which is incorporated herein by reference in itsentirety, describes some systems and methods associated with theclinical risk alerts (or alarms) described herein. In some embodiments,clinical risk alerts can be triggered for a predetermined time period toallow for the user to attend to his/her condition. Additionally, theclinical risk alerts can be de-activated when leaving a clinical riskzone so as not to annoy the patient by repeated clinical alarms (e.g.,visual, audible or vibratory), when the patient's condition isimproving. In some embodiments, dynamic and intelligent estimationdetermines a possibility of the patient avoiding clinical risk, based onthe analyte concentration, the rate of change, and other aspects of thedynamic and intelligent estimative algorithms. If there is minimal or nopossibility of avoiding the clinical risk, a clinical risk alert will betriggered. However, if there is a possibility of avoiding the clinicalrisk, the system is configured to wait a predetermined amount of timeand re-analyze the possibility of avoiding the clinical risk. In someembodiments, when there is a possibility of avoiding the clinical risk,the system is further configured to provide targets, therapyrecommendations, or other information that can aid the patient inproactively avoiding the clinical risk.

In some embodiments, the sensor electronics module is configured tosearch for one or more display devices within communication range of thesensor electronics module and to wirelessly communicate sensorinformation (e.g., a data package including displayable sensorinformation, one or more alarm conditions, and/or other alarminformation) thereto. Accordingly, the display device is configured todisplay at least some of the sensor information and/or alarm the host(and/or care taker), wherein the alarm mechanism is located on thedisplay device.

In some embodiments, the sensor electronics module is configured toprovide one or a plurality of different alarms via the sensorelectronics module and/or via transmission of a data packagingindicating an alarm should be initiated by one or a plurality of displaydevices (e.g., sequentially and/or simultaneously). In some embodiments,the sensor electronics module determines which of the one or more alarmsto trigger based on one or more alerts that are triggered. For example,when an alert triggers that indicates severe hypoglycemia, the sensorelectronics module can perform multiple actions, such as activating analarm on the sensor electronics module, transmitting a data package to asmall (key fob) indicating activation of an alarm on the display, andtransmitting a data package as a text message to a care provider. As anexample, a text message can appear on a small (key fob) display, cellphone, pager device, and/or the like, including displayable sensorinformation that indicates the host's condition (e.g., “severehypoglycemia”).

In some embodiments, the sensor electronics module is configured to waita time period for the host to respond to a triggered alert (e.g., bypressing or selecting a snooze and/or off function and/or button on thesensor electronics module and/or a display device), after whichadditional alerts are triggered (e.g., in an escalating manner) untilone or more alerts are responded to. In some embodiments, the sensorelectronics module is configured to send control signals (e.g., a stopsignal) to a medical device associated with an alarm condition (e.g.,hypoglycemia), such as an insulin pump, wherein the stop alert triggersa stop of insulin delivery via the pump.

In some embodiments, the sensor electronics module is configured todirectly, systematically, simultaneously (e.g., via broadcasting),regularly, periodically, randomly, on-demand, in response to a query(from the display device), based on alerts or alarms, and/or the liketransmit alarm information. In some embodiments, the system furtherincludes a repeater such that the wireless communication distance of thesensor electronics module can be increased, for example, to 10, 20, 30,50 75, 100, 150, or 200 meters or more, wherein the repeater isconfigured to repeat a wireless communication from the sensorelectronics module to the display device located remotely from thesensor electronics module. A repeater can be useful to families havingchildren with diabetes. For example, to allow a parent to carry, orplace in a stationary position, a display device, such as in a largehouse wherein the parents sleep at a distance from the child.

Display Devices

In some embodiments, the sensor electronics module is configured tosearch for and/or attempt wireless communication with a display devicefrom a list of display devices. In some embodiments, the sensorelectronics module is configured to search for and/or attempt wirelesscommunication with a list of display devices in a predetermined and/orprogrammable order (e.g., grading and/or escalating), for example,wherein a failed attempt at communication with and/or alarming with afirst display device triggers an attempt at communication with and/oralarming with a second display device, and so on. In one exemplaryembodiment, the sensor electronics module is configured to search forand attempt to alarm a host or care provider sequentially using a listof display devices, such as: 1) a default display device, 2) a key fobdevice, 3) a cell phone (via auditory and/or visual methods, such as,text message to the host and/or care provider, voice message to the hostand/or care provider, and/or 911).

Depending on the embodiment, one or more display devices that receivedata packages from the sensor electronics module are “dummy displays”,wherein they display the displayable sensor information received fromthe sensor electronics module without additional processing (e.g.,prospective algorithmic processing necessary for real-time display ofsensor information). In some embodiments, the displayable sensorinformation comprises transformed sensor data that does not requireprocessing by the display device prior to display of the displayablesensor information. Some display devices may comprise software includingdisplay instructions (software programming comprising instructionsconfigured to display the displayable sensor information and optionallyquery the sensor electronics module to obtain the displayable sensorinformation) configured to enable display of the displayable sensorinformation thereon. In some embodiments, the display device isprogrammed with the display instructions at the manufacturer and caninclude security and/or authentication to avoid plagiarism of thedisplay device. In some embodiments, a display device is configured todisplay the displayable sensor information via a downloadable program(for example, a downloadable Java Script via the internet), such thatany display device that supports downloading of a program (for example,any display device that supports Java applets) therefore can beconfigured to display displayable sensor information (e.g., mobilephones, PDAs, PCs and the like).

In some embodiments, certain display devices may be in direct wirelesscommunication with the sensor electronics module, however intermediatenetwork hardware, firmware, and/or software can be included within thedirect wireless communication. In some embodiments, a repeater (e.g., aBluetooth repeater) can be used to re-transmit the transmitteddisplayable sensor information to a location farther away than theimmediate range of the telemetry module of the sensor electronicsmodule, wherein the repeater enables direct wireless communication whensubstantive processing of the displayable sensor information does notoccur. In some embodiments, a receiver (e.g., Bluetooth receiver) can beused to re-transmit the transmitted displayable sensor information,possibly in a different format, such as in a text message onto a TVscreen, wherein the receiver enables direct wireless communication whensubstantive processing of the sensor information does not occur. In oneembodiment, the sensor electronics module directly wirelessly transmitsdisplayable sensor information to one or a plurality of display devices,such that the displayable sensor information transmitted from the sensorelectronics module is received by the display device withoutintermediate processing of the displayable sensor information.

In some embodiments, one or more sensors are configured to process datathrough communication with a “cloud” based processing system. Forexample, the applications for processing the sensor data may reside inone or more servers in communication with the sensor. The applicationscan be queried by the sensor for processing the data and determiningtrend or pattern information.

In one embodiment, one or more display devices comprise built-inauthentication mechanisms, wherein authentication is required forcommunication between the sensor electronics module and the displaydevice. In some embodiments, to authenticate the data communicationbetween the sensor electronics module and display devices, achallenge-response protocol, such as a password authentication isprovided, where the challenge is a request for the password and thevalid response is the correct password, such that pairing of the sensorelectronics module with the display devices can be accomplished by theuser and/or manufacturer via the password. However, any knownauthentication system or method useful for telemetry devices can be usedwith the preferred embodiments.

In some embodiments, one or more display devices are configured to querythe sensor electronics module for displayable sensor information,wherein the display device acts as a master device requesting sensorinformation from the sensor electronics module (e.g., a slave device)on-demand, for example, in response to a query. In some embodiments, thesensor electronics module is configured for periodic, systematic,regular, and/or periodic transmission of sensor information to one ormore display devices (for example, every 1, 2, 5, or 10 minutes ormore). In some embodiments, the sensor electronics module is configuredto transmit data packages associated with a triggered alert (e.g.,triggered by one or more alert conditions). However, any combination ofthe above described statuses of data transmission can be implementedwith any combination of paired sensor electronics module and displaydevice(s). For example, one or more display devices can be configuredfor querying the sensor electronics module database and for receivingalarm information triggered by one or more alarm conditions being met.Additionally, the sensor electronics module can be configured forperiodic transmission of sensor information to one or more displaydevices (the same or different display devices as described in theprevious example), whereby a system can include display devices thatfunction differently with regard to how they obtain sensor information.

In some embodiments, as described in more detail elsewhere herein, adisplay device is configured to query the data storage memory in thesensor electronics module for certain types of data content, includingdirect queries into a database in the sensor electronics module's memoryand/or requests for configured or configurable packages of data contenttherefrom; namely, the data stored in the sensor electronics module isconfigurable, queryable, predetermined, and/or pre-packaged, based onthe display device with which the sensor electronics module iscommunicating. In some additional or alternative embodiments, the sensorelectronics module generates the displayable sensor information based onits knowledge of which display device is to receive a particulartransmission. Additionally, some display devices are capable ofobtaining calibration information and wirelessly transmitting thecalibration information to the sensor electronics module, such asthrough manual entry of the calibration information, automatic deliveryof the calibration information, and/or an integral reference analytemonitor incorporated into the display device. U.S. Pat. No. 7,774,145,U.S. Publ. No. 2007-0203966-A1, U.S. Publ. No. 2007-0208245-A1, and U.S.Pat. No. 7,519,408, each of which is incorporated herein by reference inits entirety, describe systems and methods for providing an integralreference analyte monitor incorporated into a display device and/orother calibration methods that can be implemented with the preferredembodiments.

In general, a plurality of display devices (e.g., a small (key fob)display device, a larger (hand-held) display device, a mobile phone, areference analyte monitor, a drug delivery device, a medical device anda personal computer) are configured to wirelessly communicate with thesensor electronics module, wherein the one or more display devices areconfigured to display at least some of the displayable sensorinformation wirelessly communicated from the sensor electronics module,wherein displayable sensor information includes sensor data, such as rawdata and/or transformed sensor data, such as analyte concentrationvalues, rate of change information, trend information, alertinformation, sensor diagnostic information and/or calibrationinformation, for example.

Small (Key Fob) Display Device

In some embodiments, one the plurality of display devices is a small(e.g., key fob) display device 14 (FIG. 1 ) that is configured todisplay at least some of the sensor information, such as an analyteconcentration value and a trend arrow. In general, a key fob device is asmall hardware device with a built-in authentication mechanism sized tofit on a key chain. However, any small display device 14 can beconfigured with the functionality as described herein with reference tothe key fob device 14, including a wrist band, a hang tag, a belt, anecklace, a pendent, a piece of jewelry, an adhesive patch, a pager, anidentification (ID) card, and the like, all of which are included by thephrase “small display device” and/or “key fob device” herein.

In general, the key fob device 14 includes electronics configured toreceive and display displayable sensor information (and optionallyconfigured to query the sensor electronics module for the displayablesensor information). In one embodiment, the electronics include a RAMand a program storage memory configured at least to display the sensordata received from the sensor electronics module. In some embodiments,the key fob device 14 includes an alarm configured to warn a host of atriggered alert (e.g., audio, visual and/or vibratory). In someembodiments, the key fob device 14 includes a user interface, such as anLCD 602 and one or more buttons 604 that allows a user to view data,such as a numeric value and/or an arrow, to toggle through one or morescreens, to select or define one or more user parameters, to respond to(e.g., silence, snooze, turn off) an alert, and/or the like.

In some embodiments, the key fob display device has a memory (e.g., suchas in a gig stick or thumb drive) that stores sensor, drug (e.g.,insulin) and other medical information, enabling a memory stick-typefunction that allows data transfer from the sensor electronics module toanother device (e.g., a PC) and/or as a data back-up location for thesensor electronics module memory (e.g., data storage memory). In someembodiments, the key fob display device is configured to beautomatically readable by a network system upon entry into a hospital orother medical complex.

In some embodiments, the key fob display device includes a physicalconnector, such as USB port 606, to enable connection to a port (e.g.,USB) on a computer, enabling the key fob to function as a data downloaddevice (e.g., from the sensor electronics module to a PC), a telemetryconnector (e.g., Bluetooth adapter/connector for a PC), and/or enablesconfigurable settings on the key fob device (e.g., via software on thePC that allows configurable parameters such as numbers, arrows, trend,alarms, font, etc.) In some embodiments, user parameters associated withthe small (key fob) display device can be programmed into (and/ormodified) by a display device such as a personal computer, personaldigital assistant, or the like. In one embodiment, user parametersinclude contact information, alert/alarms settings (e.g., thresholds,sounds, volume, and/or the like), calibration information, font size,display preferences, defaults (e.g., screens), and/or the like.Alternatively, the small (key fob) display device can be configured fordirect programming of user parameters. In some embodiments, wherein thesmall (key fob) display device comprises a telemetry module, such asBluetooth, and a USB connector (or the like), such that the small (keyfob) display device additionally functions as telemetry adapter (e.g.,Bluetooth adapter) enabling direct wireless communication between thesensor electronics module and the PC, for example, wherein the PC doesnot include the appropriate telemetry adapter therein.

Large (Hand-Held) Display Device

In some embodiments, one the plurality of display devices is a hand-helddisplay device 16 (FIG. 1 ) configured to display sensor informationincluding an analyte concentration and a graphical representation of theanalyte concentration over time. In general, the hand-held displaydevice comprises a display 608 sufficiently large to display a graphicalrepresentation 612 of the sensor data over a time period, such as aprevious 1, 3, 5, 6, 9, 12, 18, or 24-hours of sensor data. In someembodiments, the hand-held device 16 is configured to display a trendgraph or other graphical representation, a numeric value, an arrow,and/or to alarm the host. U.S. Publ. No. 2005-0203360-A1, which isincorporated herein by reference in its entirety, describes andillustrates some examples of display of data on a hand-held displaydevice. Although FIG. 1 illustrates one embodiment of a hand-helddisplay device, the hand-held device can be any single applicationdevice or multi-application device, such as mobile phone, a palm-topcomputer, a PDA, portable media player (e.g., iPod, MP3 player), a bloodglucose meter, an insulin pump, and/or the like.

In some embodiments, a mobile phone (or PDA) is configured to display(as described above) and/or relay sensor information, such as via avoice or text message to the host and/or the host's care provider. Insome embodiments, the mobile phone further comprises an alarm configuredto warn a host of a triggered alert, such as in response to receiving adata package indicating triggering of the alert. Depending on theembodiment, the data package may include displayable sensor information,such as an on-screen message, text message, and/or pre-generatedgraphical representation of sensor data and/or transformed sensor data,as well as an indication of an alarm, such as an auditory alarm or avibratory alarm, that should be activated by the mobile phone.

In some embodiments, one of the display devices is a drug deliverydevice, such as an insulin pump and/or insulin pen, configured todisplay sensor information. In some embodiments, the sensor electronicsmodule is configured to wirelessly communicate sensor diagnosticinformation to the drug delivery device in order to enable to the drugdelivery device to consider (include in its calculations/algorithms) aquality, reliability and/or accuracy of sensor information for closedloop and/or semi-closed loop systems, which are described in more detailin U.S. Pat. No. 7,591,801, which is incorporated herein by reference inits entirety. In some alternative embodiments, the sensor electronicmodule is configured to wirelessly communicate with a drug deliverydevice that does not include a display, for example, in order to enablea closed loop and/or semi-closed loop system as described above.

In some embodiments, one of the display devices is a drug deliverydevice is a reference analyte monitor, such as a blood glucose meter,configured to measure a reference analyte value associated with ananalyte concentration in a biological sample from the host.

Personal Computer Display Device

In some embodiments, one of the display devices is personal computer(PC) 20 (FIG. 1 ) configured to display sensor information. Preferably,the PC 20 has software installed, wherein the software enables displayand/or performs data analysis (retrospective processing) of the historicsensor information. In some embodiments, a hardware device can beprovided (not shown), wherein the hardware device (e.g., dongle/adapter)is configured to plug into a port on the PC to enable wirelesscommunication between the sensor electronics module and the PC. In someembodiments, the PC 20 is configured to set and/or modify configurableparameters of the sensor electronics module 12 and/or small (key fobdevice) 14, as described in more detail elsewhere herein.

Other Display Devices

In some embodiments, one of the display devices is an on-skin displaydevice that is splittable from, releasably attached to, and/or dockableto the sensor housing (mounting unit, sensor pod, or the like). In someembodiments, release of the on-skin display turns the sensor off; inother embodiments, the sensor housing comprises sufficient sensorelectronics to maintain sensor operation even when the on-skin displayis released from the sensor housing.

In some embodiments, one of the display devices is a secondary device,such as a heart rate monitor, a pedometer, a temperature sensor, a carinitialization device (e.g., configured to allow or disallow the car tostart and/or drive in response to at least some of the sensorinformation wirelessly communicated from the sensor electronics module(e.g., glucose value above a predetermined threshold)). In somealternative embodiments, one of the display devices is designed for analternative function device (e.g., a caller id device), wherein thesystem is configured to communicate with and/or translate displayablesensor information to a custom protocol of the alternative device suchthat displayable sensor information can be displayed on the alternativefunction device (display of caller id device).

Exemplary Configurations

FIG. 1 is a diagram illustrating one embodiment of a continuous analytesensor system 8 including a sensor electronics module 12. In theembodiment of FIG. 1 , the system includes a continuous analyte sensor10 physically connected to a sensor electronics module 12, which is indirect wireless communication with a plurality of different displaydevices 14, 16, 18, and/or 20.

In one embodiment, the sensor electronics module 12 includes electroniccircuitry associated with measuring and processing the continuousanalyte sensor data, including prospective algorithms associated withprocessing and calibration of the sensor data. The sensor electronicsmodule 12 may be physically connected to the continuous analyte sensor10 and can be integral with (non-releasably attached to) or releasablyattachable to the continuous analyte sensor 10. The sensor electronicsmodule 12 may include hardware, firmware, and/or software that enablesmeasurement of levels of the analyte via a glucose sensor, such as ananalyte sensor. For example, the sensor electronics module 12 caninclude a potentiostat, a power source for providing power to thesensor, other components useful for signal processing and data storage,and a telemetry module for transmitting data from the sensor electronicsmodule to one or more display devices. Electronics can be affixed to aprinted circuit board (PCB), or the like, and can take a variety offorms. For example, the electronics can take the form of an integratedcircuit (IC), such as an Application-Specific Integrated Circuit (ASIC),a microcontroller, and/or a processor. The sensor electronics module 12includes sensor electronics that are configured to process sensorinformation, such as sensor data, and generate transformed sensor dataand displayable sensor information. Examples of systems and methods forprocessing sensor analyte data are described in more detail herein andin U.S. Pat. Nos. 7,310,544, 6,931,327, 8,010,174, 8,233,959, U.S. Publ.No. 2007-0032706-A1, U.S. Publ. No. 2008-0033254-A1, U.S. Publ. No.2005-0203360-A1, U.S. Publ. No. 2005-0154271-A1, U.S. Publ. No.2005-0192557-A1, U.S. Publ. No. 2006-0222566-A1, U.S. Publ. No.2007-0203966-A1, and U.S. Publ. No. 2007-0208245, each of which isincorporated herein by reference in their entirety.

Referring again to FIG. 1 , a plurality of display devices (14, 16, 18,and/or 20) are configured for displaying (and/or alarming) thedisplayable sensor information that has been transmitted by the sensorelectronics module 12 (e.g., in a customized data package that istransmitted to the display devices based on their respectivepreferences). For example, the display devices are configured to displaythe displayable sensor information as it is communicated from the sensorelectronics module (e.g., in a data package that is transmitted torespective display devices), without any additional prospectiveprocessing required for calibration and real-time display of the sensordata.

In the embodiment of FIG. 1 , the plurality of display devices includesa small (key fob) display device 14, such as a wrist watch, a belt, anecklace, a pendent, a piece of jewelry, an adhesive patch, a pager, akey fob, a plastic card (e.g., credit card), an identification (ID)card, and/or the like, wherein the small display device comprises arelatively small display (e.g., smaller than the large display device)and is configured to display certain types of displayable sensorinformation (e.g., a numerical value and an arrow, in some embodiments).In some embodiments, one of the plurality of display devices is a large(hand-held) display device 16, such as a hand-held receiver device, apalm-top computer and/or the like, wherein the large display devicecomprises a relatively larger display (e.g., larger than the smalldisplay device) and is configured to display a graphical representationof the continuous sensor data (e.g., including current and historicdata). Other display devices can include other hand-held devices, suchas a cell phone or PDA 18, an insulin delivery device, a blood glucosemeter, and/or a desktop or laptop computer 20.

Because different display devices provide different user interfaces,content of the data packages (e.g., amount, format, and/or type of datato be displayed, alarms, and the like) can be customized (e.g.,programmed differently by the manufacture and/or by an end user) foreach particular display device. Accordingly, in the embodiment of FIG. 1, a plurality of different display devices are in direct wirelesscommunication with the sensor electronics module (e.g., such as anon-skin sensor electronics module 12 that is physically connected to thecontinuous analyte sensor 10) during a sensor session to enable aplurality of different types and/or levels of display and/orfunctionality associated with the displayable sensor information, whichis described in more detail elsewhere herein.

Continuous Sensor

In some embodiments, a glucose sensor comprises a continuous sensor, forexample a subcutaneous, transdermal (e.g., transcutaneous), orintravascular device. In some embodiments, the device can analyze aplurality of intermittent blood samples. The glucose sensor can use anymethod of glucose-measurement, including enzymatic, chemical, physical,electrochemical, spectrophotometric, polarimetric, calorimetric,iontophoretic, radiometric, immunochemical, and the like.

A glucose sensor can use any known method, including invasive, minimallyinvasive, and non-invasive sensing techniques (e.g., fluorescentmonitoring), to provide a data stream indicative of the concentration ofglucose in a host. The data stream is typically a raw data signal, whichis converted into a calibrated and/or filtered data stream that is usedto provide a useful value of glucose to a user, such as a patient or acaretaker (e.g., a parent, a relative, a guardian, a teacher, a doctor,a nurse, or any other individual that has an interest in the wellbeingof the host).

A glucose sensor can be any device capable of measuring theconcentration of glucose. One exemplary embodiment is described below,which utilizes an implantable glucose sensor. However, it should beunderstood that the devices and methods described herein can be appliedto any device capable of detecting a concentration of glucose andproviding an output signal that represents the concentration of glucose.

In one embodiment, the analyte sensor is an implantable glucose sensor,such as described with reference to U.S. Pat. No. 6,001,067 and U.S.Publ. No. 2005-0027463-A1. In another embodiment, the analyte sensor isa transcutaneous glucose sensor, such as described with reference toU.S. Publ. No. 2006-0020187-A1. In some alternative embodiments, anoptical, non-invasive, “continuous or quasi-continuous” glucosemeasurement device such as described by U.S. Pat. No. 6,049,727, whichis incorporated by reference herein in its entirety, can be implanted inthe body for optically measuring analyte levels.

In still other embodiments, the sensor is configured to be implanted ina host vessel or extracorporeally, such as is described in U.S. Publ.No. 2007-0027385-A1, U.S. Publ. No. 2008-0119703-A1, U.S. Publ. No.2008-0108942-A1, and U.S. Publ. No. 2007-0197890-A1, the contents ofeach of which is hereby incorporated by reference in its entirety. Inone alternative embodiment, the continuous glucose sensor comprises atranscutaneous sensor such as described in U.S. Pat. No. 6,565,509 toSay et al., for example. In another alternative embodiment, thecontinuous glucose sensor comprises a subcutaneous sensor such asdescribed with reference to U.S. Pat. No. 6,579,690 to Bonnecaze et al.or U.S. Pat. No. 6,484,046 to Say et al., for example. In anotheralternative embodiment, the continuous glucose sensor comprises arefillable subcutaneous sensor such as described with reference to U.S.Pat. No. 6,512,939 to Colvin et al., for example. In another alternativeembodiment, the continuous glucose sensor comprises an intravascularsensor such as described with reference to U.S. Pat. No. 6,477,395 toSchulman et al., for example. In another alternative embodiment, thecontinuous glucose sensor comprises an intravascular sensor such asdescribed with reference to U.S. Pat. No. 6,424,847 to Mastrototaro etal., for example.

FIGS. 2A and 2B are perspective and side views of a sensor systemincluding a mounting unit 214 and sensor electronics module 12 attachedthereto in one embodiment, shown in its functional position, including amounting unit and a sensor electronics module matingly engaged therein.In some embodiments, the mounting unit 214, also referred to as ahousing or sensor pod, comprises a base 234 adapted for fastening to ahost's skin. The base can be formed from a variety of hard or softmaterials, and can comprises a low profile for minimizing protrusion ofthe device from the host during use. In some embodiments, the base 234is formed at least partially from a flexible material, which is believedto provide numerous advantages over conventional transcutaneous sensors,which, unfortunately, can suffer from motion-related artifactsassociated with the host's movement when the host is using the device.The mounting unit 214 and/or sensor electronics module 12 can be locatedover the sensor insertion site to protect the site and/or provide aminimal footprint (utilization of surface area of the host's skin).

In some embodiments, a detachable connection between the mounting unit214 and sensor electronics module 12 is provided, which enables improvedmanufacturability, namely, the relatively inexpensive mounting unit 214can be disposed of when replacing the sensor system after its usablelife, while the relatively more expensive sensor electronics module 12can be reusable with multiple sensor systems. In some embodiments, thesensor electronics module 12 is configured with signal processing(programming), for example, configured to filter, calibrate and/or otheralgorithms useful for calibration and/or display of sensor information.However, an integral (non-detachable) sensor electronics module can beconfigured.

In some embodiments, the contacts 238 are mounted on or in a subassemblyhereinafter referred to as a contact subassembly 236 configured to fitwithin the base 234 of the mounting unit 214 and a hinge 248 that allowsthe contact subassembly 236 to pivot between a first position (forinsertion) and a second position (for use) relative to the mounting unit214. The term “hinge” as used herein is a broad term and is used in itsordinary sense, including, without limitation, to refer to any of avariety of pivoting, articulating, and/or hinging mechanisms, such as anadhesive hinge, a sliding joint, and the like; the term hinge does notnecessarily imply a fulcrum or fixed point about which the articulationoccurs. In some embodiments, the contacts 238 are formed from aconductive elastomeric material, such as a carbon black elastomer,through which the sensor 10 extends.

In certain embodiments, the mounting unit 214 is provided with anadhesive pad 208, disposed on the mounting unit's back surface andincludes a releasable backing layer. Thus, removing the backing layerand pressing the base portion 234 of the mounting unit onto the host'sskin adheres the mounting unit 214 to the host's skin. Additionally oralternatively, an adhesive pad can be placed over some or all of thesensor system after sensor insertion is complete to ensure adhesion, andoptionally to ensure an airtight seal or watertight seal around thewound exit-site (or sensor insertion site) (not shown). Appropriateadhesive pads can be chosen and designed to stretch, elongate, conformto, and/or aerate the region (e.g., host's skin). The embodimentsdescribed with reference to FIGS. 2A and 2B are described in more detailwith reference to U.S. Pat. No. 7,310,544, which is incorporated hereinby reference in its entirety. Configurations and arrangements canprovide water resistant, waterproof, and/or hermetically sealedproperties associated with the mounting unit/sensor electronics moduleembodiments described herein.

Various methods and devices that are suitable for use in conjunctionwith aspects of some embodiments are disclosed in U.S. Publ. No.2009-0240120-A1, which is incorporated herein by reference in itsentirety.

Use of Standardized Data Communication Protocols

FIG. 3 is an exemplary block diagram illustrating various elements ofone embodiment of a continuous analyte sensor system 8 and displaydevice 14, 16, 18, 20. The sensor system 8 may include a sensor 312(also designated 10 in FIG. 1 ) coupled to a processor 314 (part of item12 in FIG. 1 ) for processing and managing sensor data. The processormay be further coupled to a transceiver 316 (part of item 12 in FIG. 1 )for sending sensor data and receiving requests and commands from anexternal device, such as the display device 14, 16, 18, 20, which isused to display or otherwise provide the sensor data to a user. Thesensor system 8 may further include a memory 318 (part of item 12 inFIG. 1 ) and a real time clock 320 (part of item 12 in FIG. 1 ) forstoring and tracking sensor data. Communication protocols and associatedmodulation schemes such as Bluetooth, Zigbee™, or ANT™ for example maybe used to transmit and receive data between the sensor system 8 and thedisplay device 14, 16, 18, 20.

The display device 14, 16, 18, 20 may be used for alerting and providingsensor information to a user, and may include a processor 330 forprocessing and managing sensor data. The display device 14, 16, 18, 20may include a display 332, a memory 334, and a real time clock 336 fordisplaying, storing and tracking sensor data respectively. The displaydevice 14, 16, 18, 20 may further include a transceiver 338 forreceiving sensor data and for sending requests, instructions, and datato the sensor system 8. The transceiver 338 may further employ thecommunication protocols described above including, but not limited to,radio frequency, Bluetooth, BTLE, Zigbee™, ANT™, etc.

In some embodiments, when a standardized communication protocol is usedsuch as Bluetooth or ANT, commercially available transceiver circuitsmay be utilized that incorporate processing circuitry to handle lowlevel data communication functions such as the management of dataencoding, transmission frequencies, handshake protocols, and the like.In these embodiments, the processor 314, 330 does not need to managethese activities, but rather provides desired data values fortransmission, and manages high level functions such as power up or down,set a rate at which messages are transmitted, and the like. Instructionsand data values for performing these high level functions can beprovided to the transceiver circuits via a data bus and transferprotocol established by the manufacturer of the transceiver circuit.

The analyte sensor system 8 gathers analyte data that it periodicallysends to the display device 14, 16, 18, 20. Rather than having thetransmission and receiving circuitry continuously communicating, theanalyte sensor system 8 and display device 14, 16, 18, 20 periodicallyestablish a communication channel between them. Thus, sensor system 8can communicate via wireless transmission (e.g., ANT+, low powerBluetooth, etc.) with display device 14, 16, 18, 20 (e.g., a hand-heldcomputing device) at predetermined time intervals. In some embodiments,the duration of the predetermined time interval can be selected to belong enough so that the sensor system 8 does not consume too much powerby transmitting data more frequently than needed, yet frequent enough toprovide substantially real-time sensor information (e.g., measuredglucose values) to the display device 14, 16, 18, 20 for output (e.g.,display) to a user. The predetermined time interval may be every fiveminutes, for example. It will be appreciated that this schedule can bevaried to be any desired time interval between data transfer activity.Those times when the communication channel is established and sensordata is being transmitted may be referred to as sensor packettransmission sessions.

In between these data transfer procedures, the transceiver 316 of theanalyte sensor system 8 can be powered down or in a sleep mode toconserve battery life. To establish a communication channel, the analytesensor system 8 may send one or more message beacons every five minutes.Each message beacon may be considered an invitation for a display device14, 16, 18, 20 to establish a communication channel with the sensorsystem 8. During initial system set up, the display device 14, 16, 18,20 may listen continuously until such a message beacon is received. Whenthe beacon is successfully received, the display device 14, 16, 18, 20can acknowledge the reception to establish communication between thedevices. When the desired data communication is complete, the channelcan be broken, and the transceiver 316 of the analyte sensing system 8(and possibly the transceiver 338 of the display device 14, 16, 18, 20as well) can be powered down. After a five minute period, thetransceivers 316, 338 can be powered up again substantiallysimultaneously, and establish a new communication channel using the sameprocess to exchange any new data. This process may continue, with newcommunication channels being established at the pre-determinedintervals. To allow for some loss of synchronization between the twodevices in between transmissions, the analyte sensor system 8 may beconfigured to send a series of message beacons in a window of timearound the scheduled transmission time (e.g., 8 message beacons persecond for 4 seconds). Any one of the message beacons can be used toinitiate the establishment of a new communication channel when it isreceived by the display device 14, 16, 18, 20.

Pattern Recognition

The process of detecting patterns based on raw sensor data according tosome embodiments will be described with reference to FIG. 4 . The term“pattern” as applied to glucose data measurements and used herein refersgenerally to repeated relationships between glucose data and someadditional variable. The additional variable is often times anoccurrence of an action or condition, but can be a non-occurrence of anaction or condition, where an action can be eating or exercising forexample, and a condition can be the value of a physiological parametersuch as body temperature, or the like. A “pattern” in glucose dataexists when similar glucose values tend to be associated with similartimes, events, or conditions. In some embodiments described herein,patterns are detected and the user is made aware of their existence in auser friendly way that has not been heretofore available. A pattern mayindicate a recurring event based on factors such as time of day,overcorrection of events by a user, and/or user activity, as will bediscussed in greater detail below.

FIG. 4 illustrates a flowchart of a method 400 of detecting patternsaccording to some embodiments. The method 400 includes receiving datafrom inputs as represented by block 401. The inputs provided mayinclude, for example, data from a continuous glucose monitor (CGM data).The CGM data may be both real-time and historical data over a period oftime. For example, the data may include historical data from a periodcovering a four week interval of monitored analyte detection levels.Additionally, or alternatively, the inputs can also include a variety ofother inputs relevant to monitored analyte, such as food intake (e.g.time, amount of carbohydrates, other food related information),exercise, time of day, awake/sleep timer intervals, medicationsingested, etc. Inputs may also be entered by a user or can be receivedfrom external devices (e.g., mobile phone, personal computer, dedicatedCGM receiver, etc.) or derived from analysis of sensor data.

The method 400 may proceed by analyzing the input data for patterns asrepresented by block 402. For example, patterns can be recognized basedon predefined criteria or a set of rules defined in a data analysisapplication. Additionally, the predefined criteria or rules may bevariable and adjustable based on user input. For example, some types ofpatterns and criteria defining patterns can be selected, turned off andon, and/or modified by a user, a user's physician, a user's guardian,etc.

Some examples of the types of relationships that can be considered apattern include hypoglycemic events by time of day. Generally, thesepatterns may be identified in situations where the user tends to havelow glucose concentrations around the same time in the day. Another typeof pattern which may be identified is a “rebound high” situation. Forexample, a rebound high may be defined as a situation where a userovercorrects a hypoglycemic event by overly increasing glucose intake,thereby going into a hyperglycemic event or near hyperglycemic event.These events may be detected based on a predefined set of criteria orrules, as will be discussed in greater detail below with reference toFIGS. 5A-5E, and identified as patterns.

Patterns that may be detected include, but are not limited to, ahyperglycemic pattern, hypoglycemic pattern, patterns associated with atime of day or week, a weighted scoring for different patterns based onfrequency, sequence, and severity. Patterns may also be based on acustom sensitivity of a user, a transition from a hypoglycemic tohyperglycemic pattern, an amount of time spent in a severe event, and acombination of glucose change and time information. Detected patternsmay also be patterns of high variability of glucose data. Further, apattern may be based on a combination of previous pattern data and acurrently detected situation, whereby the combined information generatesa predictive alert.

The method 400 may proceed by outputting information based on thedetermined patterns as represented by block 403. For example, the outputcan include a real-time analysis of the inputs and determined patternsor may be a retrospective analysis of the data. The output may beprovided by a device which is external to the sensor device, such asdisplay device 14, 16, 18 or 20 discussed, for example, with respect toFIG. 1 above. The output may include a detailed report of the determinedpattern information and possible corrective actions for the user. Theoutput may also include alerts in the form of text message, audiblealert, or the like.

Pattern Detection

As discussed above, pattern detection may be based on a number offactors. In the following discussion, pattern detection will bediscussed with reference to detection of patterns based on time of dayand rebound highs. However, one of ordinary skill in the art willrecognize that pattern detection is not limited to these two types ofpatterns. Rather, these patterns serve as examples for the followingdescription of some aspects of the disclosed embodiments.

For purposes of the following discussion, a “hypoglycemic episode” is aperiod of time with at least some relatively low glucose concentrationmeasurements. A “hypoglycemic event” is a hypoglycemic episode that isdetermined to have clinical significance based on one or morecharacteristics of the episode such as duration or lowest measuredvalues. A “hypoglycemic pattern” is a set of hypoglycemic events thathave a relationship to one another, such as tending to be related in atime, action, or condition. For the purposes of pattern detection,several alternative definitions for a hypoglycemic event are possible.Prior to defining hypoglycemic events, it is relevant to examine thecharacteristics that are indicative of a hypoglycemic event which haveclinical significance. Events of clinical significance include fallingto a very low glucose level, even where the dip in glucose level occursonly for a short amount of time. Further, depending on the predefinedthreshold level, a glucose level which remains below the thresholdlevel, even for a long period of time, may not be considered aclinically relevant event. For example, if such a pattern fails torepeat over a period of several days, it may indicate that the user hasreasonable control of the glucose level, and no corrective action may benecessary. Additionally, a glucose level which remains substantiallybelow a first threshold level, while not reaching what one wouldconsider dangerous levels, for substantially long periods may indicate aneed for corrective action.

The above described glucose pattern detection methods will be describedin greater detail with reference to FIGS. 5A-5E below. FIGS. 5A-5Eillustrate example graphs of glucose levels over a time period accordingto some embodiments. For example, the graphs of FIGS. 5A-5E plotestimated glucose values (EGV) over several hours. As used herein anestimated glucose value (EGV) means a glucose value estimated by aglucose monitoring system. The glucose monitoring systems describedherein are typically continuous glucose monitoring systems, but thedisclosed embodiment are not limited to just continuous glucosemonitoring systems. Also, an estimated glucose value can be a raw datavalue, calibrated data value, filtered data value or the like. Returningto FIGS. 5A-5E, for the purposes of discussion, two threshold levels forevaluating the EGV values are assumed. These thresholds include a firstthreshold value TH1 and a second threshold TH2 which is lower than thefirst threshold TH1. For example, TH2 may be a low EGV value, such asabout 55 and TH1 may correspond to a higher threshold such as an EVGvalue of about 80.

With reference to FIGS. 5A-5E, analysis of what may be considered a“hypoglycemic event” will be discussed. Based on this analysis, adefinition of terms that that are helpful in specifying a clinicallyrelevant hypoglycemic event may be developed.

For example, the definition of the start point and end point of anepisode may be defined with reference to FIGS. 5A-5E. The start of anepisode may be considered to occur when the EGV value falls below thefirst threshold TH1 for the first time. The end of an episode may beconsidered to occur when the EGV has been come back up to a normallevel. However, the end of an episode may not necessarily correspond tothe time at which the EGV value reaches a level above the firstthreshold value TH1. As will be discussed below, there can be a fewchoices for deciding when the episode has ended.

These choices include, for example, a time at which the EGV has beenabove TH1 for more than a certain period of time (e.g. about 45minutes). This may be referred to as “sufficient time” end pointdetermination. Additionally or alternatively, the end of an episode maybe defined as the time at which the EGV reaches a certain high value.This can be defined as a point at which the EGV reaches a value ofTH1+D1 for some predetermined offset value D1. This may be referred toas “sufficient value” end point determination.

Further, as another example, the end of an episode may be determinedrelative to a nadir value. As one example, a nadir value may becalculated by computing, at a target point, the average EGV using aninterval (e.g. 30 minute window of data) around the target point. Thecalculation may be performed at every point of an EGV graph. The nadircan be defined as the first point in the episode where the average isthe lowest. The end of an episode may be determined if the EGV rises bymore than a predetermined value D2 from a calculated nadir value whilealso being above the first threshold value TH1. This may be referred toas “nadir offset” end point determination.

The latter two criteria, namely, the sufficient time end pointdetermination and the nadir offset end point determination criteria, arebased on the assumption that if the EGV rises sufficiently, then it islikely that the user took some action which resulted in the increase ofEGV. Therefore, it may be concluded that any subsequent low is due to adifferent cause and corresponds to a different event.

Qualifying an Episode as an “Event”

The above definition of an episode is a general one and it is possiblethat not all such episodes are clinically significant. The followingdiscussion focuses on detecting episodes that are clinicallysignificant.

First, however, a definition of “average difference” in an episode isprovided. The average difference may be defined as the average value ofa period in an episode which identifies how far the EGV has been belowthe threshold value of TH1 on average during the episode. Moreprecisely, the average difference (AD) may be defined by Equation 1below:AD=(ΣTH1−EGV(P))/X  Eq. (1)where P corresponds to the subset of points that include all pointsduring the episode that are below the first threshold value TH1, and Xcorresponds to the total number of points in the subset P.

The following description identifies those episodes that may be ofclinical value, and may be identified as a hypoglycemic event. Accordingto a first example, with reference to FIG. 5A, the EGV value falls belowthe first threshold TH1 but remains above the second threshold TH2.Since the EGV remains near the first threshold value TH1, the averagedifference AD is relatively small. A set of predetermined averagedifference thresholds AD_(TH1) and AD_(TH2) may be defined fordetermining whether a calculated average difference value for a givenepisode qualifies as a hypoglycemic even. The first average differencethreshold AD_(TH1) may be greater than AD_(TH2). In addition, Forexample, AD_(TH1) may be equal to a value of 15 and AD_(TH2) may beequal to a value of 10. In addition, a first time interval TP₁ may bepredefined for comparison with a time at which the EGV remains below thefirst threshold value TH1. For example, TP₁ may be set to a value of 10hours, but is not limited thereto.

If the average difference AD of an episode is more than AD_(TH2) (e.g.10) and the total time that the EGV is below TH1 is more than the timeperiod TP₁, then the episode may be considered a hypoglycemic event.Also, in one implementation, if the average difference AD is more thanthe first average difference threshold AD_(TH1) (e.g. 15), then theepisode may be considered a hypoglycemic event regardless of the totaltime the EGV is below TH1. That is, in the latter case, the total timethat an EGV is below TH1 may be disregarded because the averagedifference AD is so large. However, in another implementation, a totaltime the EVG remains below the first threshold value TH1 may be a factorin determining whether or not an episode is considered a hypoglycemicevent when the average difference AD is more than the first averagedifference AD_(TH1). For example, an episode may be considered ahypoglycemic event if the average difference AD is more than the firstaverage difference threshold AD_(TH1) (e.g. 15) and the EVG remainsbelow the first threshold value TH1 and/or second threshold value TH2for more than a predetermined threshold amount of time TP₂, which may beless than TP₁.

As a non-limiting example with reference to FIG. 5A, the averagedifference AD may correspond to a value of 5, while the period of timethat the EGV value remains below the first threshold value TH1 may beequal to about 1 hour. In this example, even though the time period ofFIG. 5A may be long, the episode of FIG. 5A is not classified as ahypoglycemic event since the average difference AD is low. That is,since the EGV remains near the first threshold TH1, from a clinicalperspective, no corrective action may be necessary by the user.

As a non-limiting example with reference to FIG. 5B, the averagedifference AD may correspond to a value of about 12, and the time periodthat the EGV is below TH1 may be equal to about 2 hours. As a result, inthis example the episode of FIG. 5B may be classified as a hypoglycemicevent since the average difference AD is greater than AD_(TH2) and thetime period is greater than TP₁.

According to another non-limiting example, with reference to FIG. 5C, ifthe EGV falls below TH2 even for a very short period of time, then theepisode may be classified as a hypoglycemic event. In this case theaverage difference may be disregarded.

As a non-limiting example with reference to FIG. 5D, the averagedifference AD may be equal to a high value (e.g. about 20) and the timeperiod that the EGV is below the first threshold TH1 may be relativelyshort (e.g. about 20 minutes). As a result, the episode may beclassified as a hypoglycemic event since the average difference AD isgreater than AD_(TH1), even though the time that the EGV is below TH1 isless than TP₁.

Equation 1 above defines the average difference AD as a simple sum ofdifferences TH1−EGV over a subset of points below TH1. According to someembodiments, the average difference may be amplified by using a power ofindividual differences rather than the simple difference calculation ofEquation 1 above. Further, one of ordinary skill in the art willrecognize that another function may be used to calculate the averagedifference AD. For example, a function which maps the difference to aweight value and bases the detection criterion on the average weight maybe used.

In calculating the differences, the following choices may also beconsidered: first, an average over all the points of the episode,assuming a zero value for the difference at points where EGV is aboveTP₁; second, an average over only those points where the EGV was belowTP₁.

Segment in an Episode

The concept of a segment in an episode may also be used in analyzingwhether an episode may qualify as an event. Since an episode may havemany points that are close to TH1 (or even above TH1), identifying partsof the episode that are significant may be desirable. A segment may bedefined as a portion of an episode that corresponds to the mostclinically significant data. For example, a segment may be defined as aset of consecutive points in an episode where the EGV is below TH1.Further, if within the set of such points, the difference (TH1−EGV) isless than (TH1−TH2)*0.1, then such points may be excluded from thesegment.

When the average difference is computed, the average difference overeach of the segments may be used and the maximum of the averagedifference values may be used to characterize an episode.

To illustrate the concept of a segment, an example will now be describedwith reference to FIG. 5E. Here, a segment is defined as the portionbetween time point A and the time point B as identified by the verticallines in FIG. 5E. The segment bounded by lines A and B in this examplecorresponds to the portion of the episode where the difference (TH1−EGV)is less than (TH1−TH2)*0.1. As illustrated, the segment can beconsidered to include the most severe portion of the episode anddisregard the data points where the EGV is close to the first thresholdvalue TH1. As a result, the analysis of the episode may focus on themost clinically relevant data points. The episode may then qualify as anevent if the segment portions of the episode qualify the episode as anevent using, for example, any of the event qualifying conditionsdiscussed above.

In the foregoing description, one of ordinary skill in the art willrecognize that the number of threshold values is not limited to twothreshold values as discussed above with respect to FIGS. 5A-5E. Rather,any number of threshold values TH may be defined for analyzing the data.Further, the number of average difference values AD and time periods TPmay also include any number of predefined values. One of ordinary skillin the art will recognize that the above number of thresholds, number oftime periods, and predetermined values for each threshold and timeperiod are illustrative in nature and are used for ease of descriptionof some aspects of the disclosure.

Further, any of the above described threshold values and time periodsare variables that may be user configurable (e.g. by manufacturer,patients, health care providers, guardians, etc.). These include, forexample, the first time period TP₁, first threshold value TH1, thesecond threshold value TH2, the first average difference value AD_(TH1),and the second average difference value AD_(TH2).

Distance Between Two Events

The distance, or the difference in time of modal day, between two eventsmay also present clinically important data. There can be severaldefinitions for the distance. For example, the distance may be definedas the distance between events H1 and H2 as the time span between themid-points of events H1 and H2, or the time span between the closestpoints of H1 and H2. Using the latter definition, if the two events H1and H2 are overlapping, then the distance may be defined as zero. If theevents do not overlap, the distance may be defined as the time betweenthe end of one event H1 and the beginning of the other event H2.

The distance may also be defined using the nadirs in H1 and H2. With theconcept of a segment defined above, a particular time in an event may beat the midpoint of the deepest segment in the event. The distancebetween H1 and H2 may then be defined as the time span between themid-points of the deepest segments in H1 and H2.

Repetitive Pattern Identification

Given a time interval TS, a pattern may be defined as a set S of eventssuch that the distance between any two events in the set S is less thana time interval TS. Further, in some embodiments, no two events from thesame day may be selected for inclusion in the set. In some embodiments,if H1 and H2 are two events such that the actual time difference betweenthe two events H1 and H2 (not the modal day time difference) is lessthan 12 hours, then the events are not added to set of patterns S.

With these baselines, once TS and the number of events needed torecognize a pattern of events is specified, all the events may bescanned to identify repetitive patterns. This initial grouping may thenprovide a few candidate sets for patterns. These candidate sets may thenbe subsequently filtered for identifying patterns to output to the user.

For example, a set of events A, B, C, D, E, and F may occur on differentdays such that the events are each one hour apart on respective days.Further assuming TS is 2 hours and the number of events required for apattern is three, the following sets of potential candidates may result:{A, B, C}, {B, C, D}, {C, D, E}, and {D, E, F}. From these initial sets,the sets which should be identified as patterns may be selected.

One or more of the following filters may be applied for selecting thefinal patterns or reducing the number of patterns prior to selectingfinal patterns from candidate sets:

-   -   1. Selecting the earliest set: Here, the sets may be sorted        based on the earliest modal time of an event in each set. The        earliest set is selected and all the events included the        earliest set are deleted from the remaining candidate sets. The        filter then iteratively proceeds in a likewise fashion with the        next earliest remaining candidate set until every remaining        candidate set has been selected. Note that a candidate set that        no longer qualifies as a set under the predefined rules may be        eliminated.    -   2. Prioritized by time of day: Using this filter, a certain        priority can be assigned to the candidate sets based on the time        of day. For example, the modal day can be divided into eight        intervals of three hours each. Each interval is assigned a        weight or score. Based on the assigned weight or score, the        candidate sets may be prioritized by the weight or score of the        events in the pattern, such as a sum, an average, a median or        the like of the weights or scores in a candidate set. The        highest priority set is then selected, and the events included        in the selected set are deleted from the remaining sets. The        next highest priority set may then be selected based on the        assigned weights of the events remaining in its set, and all        events in the currently selected set that are also present in        other candidate sets are eliminated. The process may be        iteratively performed until all candidate sets are processed in        this fashion. Note that a candidate set that no longer qualifies        as a set under the predefined rules may be eliminated.    -   3. Prioritized by type of events: Another filter may assign a        weight or score to each event based on the severity of the        event. For example, a deep event may be assigned a high weight        or score. For example, an event based on the average difference        may be assigned a weight that is proportional to the average        difference. Additionally, or alternatively, the weight or score        may also take into account the duration of the event. The        candidate sets get prioritized by summing, averaging or taking        the median value of the assigned weights or scores events in        each set. These priorities may then be used to sort the        candidate sets and eliminate candidate sets as discussed in        filters 1 and 2, above.

In some embodiments, each of the above filters are iteratively applied(e.g., filter 1, then filter 2 and finally filter 3) until either apredetermined threshold amount or fewer number of pattern sets remain oreach of the filters have been completely applied. If more than apredetermined threshold number of pattern sets remain after applying allof the filters, then a threshold number of pattern sets can be selectedas a final set based on a priority, such as a priority by time orpriority based on type of event.

Pattern Analysis by Event Detection

The previous sections define and outline exemplary methods for detectingpatterns of events, with much of the description describing hypoglycemicevents for illustrative purposes. This section further elaborates on animplementation on a computing device to detect patterns based on theabove-described methods. The process will be explained with reference toFIG. 6 .

FIG. 6 illustrates a flowchart of a pattern detection process 600according to some embodiments. The various tasks performed in connectionwith process 600 may be performed by hardware, software, firmware, orany combination thereof incorporated into one or more of computingdevices, such as one or more of sensor system 8 and display devices 14,16, 18 and 20. It should be appreciated that process 600 may include anynumber of additional or alternative tasks. The tasks shown in FIG. 6need not be performed in the illustrated order, and process 600 may beincorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Forillustrative purposes, the following description of process 600 mayrefer to elements mentioned above in connection with FIGS. 1-5 .

The pattern detection process 600 may be performed based on userrequest, or automatically. For automatic pattern detection, the processmay be performed based on any number of predetermined or user definedvariables. For example, according to some embodiments, the process 600is performed automatically (without user interaction triggering theprocess) when one or more of the following conditions have beensatisfied: (i) it has been at least one day since the process was lastperformed, (ii) no alerts are pending at this point, and (iii) theglucose monitoring system is currently between sensor packettransmission sessions (e.g., between sensor system 8 and display device14, 16, 18 20). Additionally or alternatively, a restriction that theprocess 600 is only performed at a particular time of the day (e.g., atnight time), or that that at least a certain number of days' worth ofdata (e.g., 7 days of data) is stored for analysis may be applied. Anyof these restrictions may be user configurable (e.g., allow or disallowthe restriction) depending upon user preference. In some embodiments, auser can configure the restrictions using user interface of displaydevice 14, 16, 18, or 20 discussed above with reference to FIG. 1 .

There may be additional optimizations provided while performing thepattern detection process 600 based on automatic triggering. Forexample, based on the performance of the process 600, the process may beperformed in several installments over a period of time. Further, whenthe process is performed upon user request, a display to the user ofstatus of the analysis or other information may occur while the data isbeing processed.

As illustrated in FIG. 6 , the process 600 begins by selecting astarting point (or total timeframe) of data to be analyzed in block 601.The start point may correspond to the earliest point where EGV dataexists or is available. For example, this time may be as old as onemonth from the time that the process begins, or even older, depending onthe amount of stored data. The start time may also be set to optimizeefficiency, and reduce the analysis of excessive data, by setting thestart time to a time later than the earliest point where EVG dataexists, such as one week, two weeks, three weeks, one month, two months,six months, one year or other time period, when EGV data to be analyzedis available.

If the process 600 has previously run, the start time may be set basedon the oldest episode or event used in a prior iteration of the process.For example, if during the last iteration of the process, there are anyepisodes or events that were detected but not fully processed, thestarting point of the oldest such episode or event may be used as thestarting point. If there are no unprocessed episodes or events, the timethat the process was run last may be set as the starting point.

The process proceeds to block 603, where new episodes are detected.Episodes may be detected as discussed above, such as with respect toFIGS. 5A-5E. As discussed above, new episodes may be detected byscanning the EGV records and using predefined criteria to detect thestarting and ending of episodes. For example, an episode may bedetermined to have started when the EGV falls below TH1 and stays lowfor at least 15 minutes (e.g. three data points at five minuteintervals). Because the first data point in the data being analyzed maybe part of an episode under the definitions for an episode being used,the time of the available EVG data point may be considered as the starttime of an episode. An episode may be considered to end when the EGVremains above TH1 for at least 45 minutes, or when the EGV reachesTH1+40 mg/dL.

At block 604, the detected episodes are filtered based on theircharacteristics to arrive at a set of events. As discussed above, onlythe events that are clinically relevant for identifying patterns areused for pattern detection. According to one example identifyinghypoglycemic episodes, a value of average difference thresholds AD_(TH1)and AD_(TH2) may be set for comparison with an average difference AD ofthe episode. AD_(TH2) may defined as (TH1−TH2)*0.33 and AD_(TH1) maydefined to be (TH1−TH2)*0.66. If, in an episode, the EGV reaches TH2 theepisode is characterized as a hypoglycemic event. If, in an episode,there is at least one segment where the average difference is greaterthan AD_(TH2) and the segment is longer than a predetermine time period(e.g. 40 minutes), then the episode may be characterized as hypoglycemicevent. If in an episode, there is at least one segment where the averagedifference is greater than AD_(TH1), then the episode may becharacterized as a hypoglycemic event.

At block 605, all available events to be analyzed are identified andgrouped. In one implementation, an event is determined to be availableif it was not previously identified as part of a pattern, even if theevent was previously identified as an event during a prior iteration ofprocess 600. Thus, both “old” and new events may be considered.

In some implementations, once all available events are gathered, sets ofevents may be formed and patterns determined based on the sets of eventsat block 606. Events may be grouped to form one or more sets of eventsbased any qualifying criteria discussed herein.

In one implementation, each set of events may be based on a distancebetween events. The distance based on segments may be used to define thedistance between two events. For example, the distance may be the timespan between the mid-points of the deepest segment in each event. Thedistances may then be used to determine the events that are close toeach other. Since the expected number of hypoglycemic events isgenerally small, this determination may be performed by iterativelyidentifying all events that are within a predefined distance of eachevent.

For example, if an event A is between 11 PM and 6 AM, then any eventthat is within a 4 hour window of event A will be included in the setfor event A as long as the event is within 11 PM and 6 AM. The window oftime may be variable based on the time of the event to be analyzed. Forexample, for an event A occurring between 6 AM and 11 PM, only thoseevents within predetermined window of time, such as 2 hours, of event Amay be included in the pattern set around event A. A set that has morethan a predetermined number of member events (e.g., 2, 3, 4 or moreevents in a set) may be considered a pattern.

In some implementations, process 600 can identify and delete duplicatepattern sets that may have been formed in the previous step. Forinstance, given sets {A, B, C} and {B, A, C}, then the latter set may bedeleted.

Based on the remaining pattern sets, patterns are selected for displayin step 607. It should be appreciated that there are a myriad ofapproaches that can be implemented to select patterns for display,outputting or further processing. One approach can be to simply selectall patterns. Another approach is to select all patterns if the totalnumber of patterns is less than a predetermined threshold number, andfilter the patterns using one of the filters discussed herein, forexample, until less than the predetermined threshold number remain. Afurther approach is to select only patterns that satisfy certainpredetermined conditions, such as patterns falling within a particulartime of day, patterns that follow a particular occurrence (e.g., a meal,exercise or medication administration) or strength of a pattern based ona strength metric (e.g., using a weighting or scoring of events in apattern).

In some implementations, the patterns which are selected may be based onparticular preferences indicated by user. In this way, a user can definehow many patterns the user wants to view, the strength or weakness ofpatterns to view, patterns occurring at particular times of day or afterparticular occurrences, and the like.

To illustrate, in one implementation, priorities for the remaining setsare determined and pattern sets filtered based on the priorities. Thepriorities may be assigned according to the time of the events, severityof the events or some other clinical relevance criterion, as discussedelsewhere herein. For example, a hypoglycemic event between 11 PM and 6AM may be given a priority of 2. A hypoglycemic event that crosses TH2or with average difference greater than AD_(TH1) may be given anadditional priority of 1. Further, an event that occurred within apredetermined amount of time, such as within the past day, may be givenan additional priority of 1. The priority of an entire pattern set maybe defined as the sum of the priorities of all the evens within thepattern set.

A predetermined number of the highest priority pattern sets for displayto the user may be selected. If there are more than one pattern setsthat have the same priority, then the pattern set having the earliestevent may be selected. Alternatively, the set having the most recentevent may be selected. In accordance with some embodiments, once anevent is selected for a pattern set that is shown to the user, it istaken out of the pool of events that can be used for later patterndetection in a subsequent iteration of process 600. That is, the eventis no longer available for selection in step 605 in a subsequentiteration of process 600.

At block 608, the selected patterns are outputted for display or furtherprocessing. For example, the patterns may be displayed on a userinterface, such as one of the user interfaces described in FIGS. 9-11 .In some implementations, alerts can be triggered based on the selectedpatterns instead of or in addition to displaying the selected patterns.And is some implementations, the selected patterns are processed furtherto modify some other process, such as a medication administrationroutine (e.g., insulin administration routine) and the like. Further,the selected patterns may be logged or stored in memory of the systemfor subsequent access or display to a user.

In accordance with some embodiments, after events are identified asdiscussed above in step 604, patterns may be detected at step 606 bysorting the identified events in increasing order of a time of day(hour, minute, second of the time of day) associated with the event. Thetime of day can be any way of associating a time of day with an eventdiscussed herein, including start time of the event, end time of theevent, mid-point time of the event, time corresponding to a nadir pointof the event, and the like. Once sorted by time of day, the events maybe scanned in order of the first event to the last event. At each event,all other events that are within a predetermined amount of time (e.g., 2hours) may be added together to form an event group that defines apattern. In this way, a plurality of patterns may be identified from acorresponding plurality of event groups. Information related to all orsome of the patterns can then be outputted to a user or anothercomputing device for further processing. In one embodiment, informationassociated with each pattern is displayed on a user interface. Theinformation can include the times associated with one or more members ofthe event group forming the pattern, number of members in each group, anindication of clinical relevance of one or more of the patterns, glucosevalue associated with each member of an event group, an average or meanglucose value of the members in an event group, and the like. Further,in some embodiments, the patterns can be analyzed using clinicalrelevance criteria and outputted by ranking the patterns from mostclinically relevant to least clinically relevant.

Exemplary Pattern Reporting User Interface

A user interface 1000 displaying a report that includes a chart 1002 ofglucose data points over a six day period and a chart 1004 of individualevents that were determined to form a pattern is illustrated in FIG. 10. In the chart 1004, the data points of each event in a pattern areindicated by the same symbol so that the behavior of each event in agroup can be visualized. Further, the user interface 1000 provides amenu 1006 listing a plurality of patterns identified in the glucose databeing analyzed. The patterns in the list can correspond to patternsselected using process 600, discussed above, for example. Each patterncan be selected by a user using, for example, a pointing device of userinterface 1000. The selected pattern may then be displayed in chart1004. This way, a user can view the glucose data points associated witheach event that forms the selected pattern.

In some embodiments, the threshold values and time periods discussedherein are initially set by a manufacturer, but can be modified by auser using, for example, one of the display devices 14, 16, 18 or 20discussed above with respect to FIG. 1 .

Pattern Analysis by Aggregating EGV by Time of Day

According to some embodiments, another approach for detecting patternsin glucose data (e.g., EGVs) over a period of time is to aggregate theglucose values by time of the day. According to some embodiments, themethod may include dividing the 24 hours in the day into epochs, eachepoch spanning a predetermined interval of time, such as a 1, 5, 10, 15or 20 minute interval. The EGVs for a given epoch from all days over apredetermined range are aggregated to derive a value for the particularepoch. If the aggregated value for the epoch shows that the EGV for thatepoch exceeds a predetermined threshold (e.g., falls below apredetermined value), the epoch may be flagged as a match for patternanalysis purposes.

An epoch may be chosen to have a duration equal to the periodicity ofthe sampled data or as small or as large as the granularity desired fordetection/reporting of pattern occurrences. If the sampled values have aperiodicity much smaller than the epoch duration chosen, then thesampled data can be averaged over the epoch duration as a single valueor, alternatively, the mean value of the sampled data over the epochduration can be used as the value of the epoch.

Multiple contiguous epochs may be chosen to represent the desiredduration/span to detect patterns. According to some embodiments, all theepochs that span an entire day may be chosen and then, if desired, afilter may be applied on the timestamps of the sampled values to limitwhich epochs are used in occurrence pattern detection analysis.

Epochs may also associate (or contain) the sample values that match thepattern and that match the epochs time period.

According to some aspects, the aggregate method may be used to detectpatterns of low or high glucose occurring around the same time of day.Further, patterns of rapidly falling or rising changes in glucose aroundthe same time of day may be detected.

In some implementations, epochs can be scored to facilitate detection ofparticular patterns. As mentioned above, a 24 hour period of a modal daymay be divided into equal intervals (e.g. 5 minutes intervals). Eachsuch interval is defined as an epoch. For an analysis of EGV data over Ndays, for every epoch T, a function Score (T) is defined as follows:

$\begin{matrix}{{{Score}(T)} = \left( {{\sum\limits_{0 - N}^{\;}{{TH}\; 1}} - {{EGV}(T)}} \right)} & {{Eq}.\mspace{14mu}(2)}\end{matrix}$where TH1 corresponds to a predetermined threshold. In some embodiments,the value of TH1 can be defined so values that fall above thepredetermined threshold are given a score of zero. In this manner, allEGVs that are above the TH1 threshold are given the same weight (i.e.0), thereby emphasizing EGVs that fall below the threshold TH1. Scoresfor each epoch interval are computed. For example, for a 5 minuteinterval, scores for all the 288 epochs are calculated over N days.Assuming a TH1 of 70 and N=7, for example, and the epoch to be analyzedis for a time period between 10:15 AM and 10:20 AM the following valuesfor EGV may be detected: EGV(Day1)=65, EGV(Day 2)=72, EGV (Day 3)=68,EGV (Day 4)=69, EGV (Day 5)=71, EGV (Day 6)=64, and EGV (Day 7)=66. Theepoch score for this time interval is then calculated as:Score(T)=5+0+2+1+0+6+4=18

In the above example, a summation of the simple differences between thethreshold TH1 and EGVs are used in defining the score above. However, afunction of the EGV may be used instead. For example, each EGV can beassigned a weight, and the sum or mean of the weights may then be usedto calculate the score of the epoch.

Epochs may be flagged as a match based on their scores. For example,given a predetermined threshold score D, an epoch T may be flagged as amatching epoch if Score (T)>D. As discussed above, the simple differencevalue of Equation 2 above is not the exclusive way for defining a score.Accordingly, a match may also be based on other methods depending uponhow a score is defined.

In some embodiments, a criterion that can be used for identifyingmatches is the number of days that contribute to the score at a givenepoch. For example, while performing a seven day data analysis, if anepoch includes two days for which the EGV was very low then it may beconsidered a match. However, if an epoch had an EGV that was closer toTH1, then it will be considered a match only if a majority of days (e.g.5 days) contributed to that epoch.

Patterns may be identified by scanning all the epochs for a modal daystarting at, for example, the beginning of a day (e.g., at time 00:00).At any point of time, the scanner can be in one of two states: “Inevent” or “Out of event”. The scan may start in an “Out of event” stateand as soon as the first epoch is encountered that is flagged as amatch, the scan may then enter the “In event” state. In someimplementations, the scan then transitions to an “Out of event” stateonly after the scan counts a predetermined number of epochs (e.g. nineepochs) that are not a match, or if a predetermined number of continuousepochs (e.g., 3) are not a match). Should epochs that fall on the end ofa group of “In event” epochs not be a match, the “in event” state can bemoved back to the last flagged epoch. Once all the epochs are scanned,any group of consecutive epochs that are “In event” may form onepattern.

It is appreciated that there may be patterns that start very late in oneday and cross over to the beginning of the next day. To account for suchpatterns, the same data may be juxtaposed twice when the scanning isperformed on the data to account for patterns spanning over more thanone day.

Pattern Calculator Using Data Aggregation

In some embodiments, a pattern calculator, or pattern detector, may beused to aggregate data in a set of epochs to detect patterns in the datathat match a pattern definition. The glucose data can be weightedaccording to a weighted assignment map or function, or can be the actualglucose concentration value of the data point. The pattern calculatorresult can be a set of patterns detected. Each pattern can be a sequenceof one or more epochs that contain contributing values that meet patternbound criteria and pattern threshold criteria.

FIG. 7 illustrates a process 700 for pattern detection using weightedepochs in accordance with some embodiments. The following algorithm mayalso be used to detect a pattern in a host's glucose concentration overtime. The various tasks performed in connection with process 700 may beperformed by hardware, software, firmware, or any combination thereofincorporated into one or more of computing devices, such as one or moreof sensor system 8 and display devices 14, 16, 18 and 20. It should beappreciated that process 700 may include any number of additional oralternative tasks. The tasks shown in FIG. 7 need not be performed inthe illustrated order, and process 700 may be incorporated into a morecomprehensive procedure or process having additional functionality notdescribed in detail herein. For illustrative purposes, the followingdescription of process 700 may refer to elements mentioned above inconnection with FIGS. 1-6 .

At step 702, process 700 obtains glucose data for a designated daterange. In some embodiments, the glucose data includes sampled datagenerated by sensor 10 that has been processed (e.g., filtered,averaged, and the like) and calibrated. In addition to glucoseconcentration values (e.g., EGV), the glucose data can includeinformation associated with the glucose data, such as the time when aglucose concentration value was present in a subject. This time may be ameasurement time, although there may be a time lag between a measurementtime and the associated time that a given concentration is present in atarget area of the subject. For example, there may be a time lag betweena measurement of a subcutaneous glucose concentration of a subject and ablood glucose concentration (target area) of the subject.

The glucose sensor data can be stored in computer memory of a systemimplementing process 700. The designated date range can be all glucosedata available to the system (e.g., stored in memory of the system)implementing process 700 or a portion of the available data. Forexample, the glucose data can be all glucose data stored in the systemspanning the past week, month, several months or year.

At step 704, process 700 applies bounds or filters to the glucose datato determine which data points may be considered contributors to thepattern detection. The bounds or filters may be defined by any number ofcriteria used to determine if a data point is a contributor to apattern. For example, a data point can be determined to be a contributorusing one or more of the following criteria: (i) a value range criteriadefined by upper and/or lower bounds (e.g. below a value of 70 or withina range of 70 to 39), wherein a data point's value can satisfy the valuerange criteria if the data point's value falls within the upper and/orlower bounds, for example; (ii) a time range criteria defined bystarting and/or ending times in a day (e.g. 4 AM to 10 AM), wherein adata point's value does not satisfy this criteria if the data pointcorrelates to a time (e.g., was measured at a time or is determined toindicate a user's glucose concentration at a time) that falls outside ofthe time range criteria; (iii) a day of the week criteria defined by asubset of days of the week to be analyzed (e.g. Monday through Friday orSaturday and Sunday), wherein a data point does not satisfy thiscriteria if the data point is correlates to a day of the week that fallsoutside of the day of the week criteria. Any other criteria can beapplied in combination with or separately from any of the examplesprovided above. For example, the filter may analyze only data pointsthat correlate to a time that falls within a time frame (e.g., 3 hours)of the occurrence of a particular event when information is availableindicating when the event has occurred. An occurrence of event can be atime when a user exercised, consumed a meal, administered insulin orother medication, slept or the like.

In some embodiments, a data point is considered to be a contributor onlyif the data point satisfies all of the criteria applied in step 704. Inother embodiments, a data point need not satisfy all criteria to beconsidered a contributor. For example, a data point can be considered acontributor if it satisfies criteria associated with the occurrence ofone event (e.g., consumption of a meal), but does not satisfy criteriaassociated with the occurrence of a different event (e.g., exercise).

Data points not considered to be contributors are discarded from process700 at step 706.

Data points considered to be contributors in step 704 are then assignedweighted values in step 708. In some embodiments, each glucose value ofthe contributor data points has a corresponding weighted value asdefined by a weighted assignment function or map. Accordingly, eachcontributor can be assigned a weighted value as defined by a weightedassignment function or map (also referred to herein as “mapping aweighted value to a contributor data point”). In some embodiments, theweighted assignment function or map can be embodied electronically in alookup table.

Exemplary weighted assignment maps are illustrated in FIGS. 8A-8D. InFIGS. 8A-8D, the x-axis is a range of glucose values and the y-axis arecorresponding assigned weighted values. The range of glucose valuesalong the x-axis need only encompass the range of glucose values ofvalue criteria applied in step 704, should a value criteria be appliedin the bounds step 704, because values outside of the value criteriarange should not be considered contributors and hence not be consideredin the weighted assignment step 708 of process 700. In someimplementations, the weighted values are scaled from 0 to about 1, wherea higher weight is given to values considered to be more clinicallyrelevant. However, it is appreciated that there are a myriad ofdifferent ways to assign weighted values.

While FIGS. 8A-8D illustrate weighted maps in column chart format, it isunderstood that the weighted maps can be in other formats, such as aline chart format. Regardless of the format of a weighted assignmentmap, a data point determined to be a contributor in step 704 can beassigned the weighted value associated with the glucose concentrationvalue as defined in the weighted assignment map. It should also beappreciated that a mathematical function can be used to describe adesired weights to corresponding values, and such a mathematicalfunction can be used instead of a map.

FIG. 8A illustrates a weighted assignment map which is designed to besensitive to sever hypoglycemic events. As illustrated, the map exhibitsan exponential chart of the weighted glucose values, where lower glucosevalues are assigned exponentially higher weighted values than the higherglucose values. Indeed, values 50 and above in the map of FIG. 8A areassigned a weighted value of 0.

FIG. 8B is an exemplary weighted map according to an all or nothingapproach. In FIG. 7B, all values that are below a glucose value of 55are given a weight of 1, while any glucose values above a value of 55are given a 0 or weight.

FIG. 8C is a weighted assignment map designed to have a non-linerpolynomial approximation of clinical importance. The polynomial functionmay be predefined to identify glucose data of clinical importance. Inthe example of FIG. 8C, data points having values in the range of about60 to 70 are assigned low weighted values to indicate that those valueshave relatively low clinical importance—albeit still some clinicalimportance. Data points are assigned increasingly higher weighted valuesalong the range of about 60 to 50 to indicate that glucose concentrationchanges in this range are clinically important. That is, for example, aglucose value of 60 is notably, clinically different than a glucosevalue of 50. Further, data points having a value from about 50 to 39 areassigned large weighted values, although the values change relativelylittle across this range. This reflects a presumption that while thesedata values are determined to be of high clinical importance, theclinical relevance across this range is relatively the same.

FIG. 8D illustrates a combination of three exemplary weighted assignmentmaps: a Low 802, Target 804, and High 806 map. The combined map 8D hasan x-axis that ranges from 39 to 401 to illustrate that some embodimentscan recognize patterns across a wide spectrum of glucose data, not justin low glucose ranges. Although maps 802, 804 and 806 could be usedtogether in process 700, it should be understood that the exemplaryprocess 700 would not be able to distinguish between data points thatcontribute to the respective low, target and high patterns. Instead, itis intended that process 700 be repeated using each map 802, 804 and 806separately so that separate low, target and high patterns can beidentified.

Further to FIG. 7 , process 700 continues with assigning eachcontributor to a corresponding epoch in step 710. As discussed above, anepoch spans a predetermined amount of time of a day, such as a 5, 10, 20or 30 minute interval of time. While the interval of each epoch cancorrespond to the sample interval of the glucose data being applied inprocess 700, it is appreciated that the epoch interval need not belimited by the sample interval.

As an illustrative, a non-limiting example of process 700, a contributordata point that is representative of a host's glucose concentration at10:27 a.m. (as indicated by its timestamp, for example) would beassigned to an epoch that spans that time, such as an epoch spanningfrom 10:25 a.m. to 10:30 a.m. in the example of epochs spanning 5 minuteintervals. In addition, all other contributor data points representativeof a host's glucose concentration measured between 10:25 a.m. to 10:30a.m. are assigned to that same epoch.

Thus, step 710 can be said to effectively group contributors accordingto times of the day being analyzed.

Next, in step 712, pattern thresholds can be applied to the epochs.Pattern thresholds can include one or more or the following: a thresholdminimum number of contributors in an epoch, a threshold average weightedvalue of the contributors in the epoch, a threshold medium weightedvalue of the contributors in the epoch, a threshold sum of the weightedvalues of the contributors in the epoch, a threshold average differenceof the weighted values of the contributors in the epoch, a thresholdstandard deviation value of the weighted values of the contributors inthe epoch, and a threshold correlation value. The pattern thresholds canalso be defined in terms of percentages, such that the patternthresholds are defined based on the bounds applied in step 704. Forexample, should the bounds applied in step 704 of process 700 includecriteria of data points spanning one week, a pattern threshold of 55% ofthe time can correspond to a minimum threshold of 4 contributors.

In step 714, process 700 matches epochs based on the pattern thresholdsapplied at step 712. In some embodiments, an epoch is determined to be amatch only if all of the pattern thresholds are satisfied. In otherembodiments, all pattern thresholds need not be satisfied for an epochto be considered a match; for example only one or some number less thanall of a plurality of pattern thresholds need be satisfied.

As an illustrative example of step 714, pattern thresholds can bedefined as a minimum of three contributors in an epoch with the averagevalue of the contributors in the epoch exceeding 0.50. In such anexample, an epoch would be determined to be a match if it has three ormore contributors and the average weighted value of the contributors inthat epoch exceeds 0.50.

Process 700 can then identify pattern occurrences in step 716. A varietyof ways can be used to identify a pattern. In some implementations, eachmatching epoch can be considered a pattern. In some implementations, apattern is identified when there is a predetermined minimum number ofcontiguous matching epochs; for example, two or more contiguous matchingepochs. In some implementations, a pattern does not need to have astrictly contiguous set of epochs. Instead, a predetermined number ofnon-matching epochs or a ratio of matching to non-matching epochs may beallowed within a pattern or before pattern is determined to end. In someimplementations, a first threshold number (e.g., two, three, or more)contiguous epochs starts a pattern and the pattern is determined to endonly after a second threshold number (which can be the same of differentfrom the first threshold number) contiguous non-matching epochs areidentified. Some implementations apply a combination of any of the abovecriteria for determining a pattern.

Step 718 outputs information associated with the identified pattern(s).For example, start and end times of an identified pattern may bereported to a user as the assigned starting and ending times of thefirst and last epoch in the pattern sequence. Attributes or propertiesof a pattern can also be outputted. The attributes or properties can bedefined by any calculation or statistic related to the contributors inthe occurrence. These calculations may be performed on the sampledvalues and/or weighted values of the contributors and then be used tocompare or describe the resulting patterns.

Further, information associated with the patterns can be outputted to auser interface of a computing device, such as one or more of devices 14,16, 18 and 20 of FIG. 1 , in the form of alerts or reports. Informationassociated with the identified patterns can also be outputted to amedication administration device that can use the pattern information toform or modify a medication administration routine. For example, patterninformation can be sent to an insulin pump controller that forms ormodifies an insulin administration routine based on the patterninformation, which can include administering more or less insulin duringcertain times of the day.

While the above described example of process 700 assigns weighted valuesto contributor data points at step 708, in some embodiments, contributordata points are not weighted, but rather retain their glucose value.Pattern thresholds at step 712 can then be defined based on glucosevalues rather than weighted values.

Based on the above described aggregation process 700, a significantreduction in complexity can be achieved by allowing each value to have aweighted contribution before being excluded from analysis and thusallowing multiple contributors to have a significant aggregate valuewhere any single value may not have been previously consideredsignificant on its own. Consideration of which values are near in timeto each other may also be delayed until after all values havecontributed. The resulting weighted aggregation can be more easilyinterpreted when displayed to a user and can be more easily scanned foroccurrences above a desired threshold for a pattern match.

Furthermore, based on the above methods, the strength of a glucosevalue's contribution to a pattern may be reduced to a simple weightednumeric assignment. The weighted assignment map can be tunedspecifically to match clinical relevance as determined by trainedprofessionals or desired by a user, and where the map can support acomplex non-linear curve that more closely matches clinical relevanceand/or user specific factors and preferences. Different weightedassignment maps can be chosen for different glucose ranges to match eachrange's unique and different clinical needs. Weighted assignment mapscan be scaled or amplified to increase or decrease their sensitivity todetecting a particular pattern.

According to some embodiments, the aggregate contribution of glucosevalues within an epoch of time is easily displayed and can be easilycompared or ranked with other epochs. This concept of condensed volumeof information can be more understandably presented to users.

Further, the above methods may eliminate the need to define or detectindividual episodes within a single day when the goal is only to detectcoincidental patterns of glucose over time (e.g. multiple days).However, the techniques described above can also be used to detectindividual episodes, e.g. a single day pattern of glucose values. Somepotential advantages can be realized when using the weighted values todetermine inclusion in the episode and using the accumulated values as avolume when determining an episode's rank or importance.

Example User Interface for Reporting and Displaying Patterns

In some embodiments, a user interface of secondary display device 14,16, 18, 20 may be used to configure and modify settings of process 700and output results of process 700. For instance, not only can a userview results of the patterns detected using process 700 in a convenientfashion, but the user can use the user interface to select or modifybound criteria, select of modify weighted assignment map(s) and selector modify pattern thresholds.

FIG. 9 illustrates an example of a modal day chart 900 and differentweighted aggregation charts 902, 904 and 906 that can be displayed on auser interface according to some embodiments.

The modal day chart 900 and weighted aggregation charts 902, 904 and 906each superimpose a plurality of days (seven days shown in FIG. 9 ) ofglucose value measurements onto a single modal day 24 hour period withthe same times of day aligned for each of the days of the charts. Themodal day chart 900 illustrates traces of seven different days ofglucose monitoring measurements, each data point in a trace indicativeof a host's glucose concentration at that time. Charts 902, 904 and 906illustrate aggregated data and patterns based on the three differentweighted assignment maps of FIG. 8D. Chart 902 is associated with theHigh assignment map 806, chart 904 is associated with the Targetassignment map 804 and chart 906 is associated with the Low assignmentmap 802.

In some embodiments, each epoch may be plotted in a bar graph format,where the length of each bar represents the weighted sum of thecontributors (aggregation) at a given time. In some implementations,other statistical algorithms can be applied to the contributors insteadof a summation, such as averaging, applying a mean, determining avariation, and the like, with the result of the statistical analysisbeing displayed in chart 902, 904 or 906.

Charts 902, 904 and 906 of FIG. 9 illustrate the aggregated weightedcontributors in bar graph form. The length of the bar graphs canindicate a relative strength of the contributors at the respective timesof day. For example, chart 902 illustrates that a user's glucoseconcentration tends to be relatively high around 2:40 to 3:00 a.m.,chart 904 illustrates that a user's blood glucose concentration tends tofall within a target range from about 7:30 a.m. to 10:40 a.m., althoughthe glucose concentration can be relatively low from 9:00 a.m. to 10:20a.m. as illustrated in chart 906. Further, a user's blood glucoseconcentration tends to be relatively low from about 12:00 p.m. to 3:00p.m.

Charts 902 and 906 also highlight detected patterns 908 and 910. Thepatterns may be detected using process 700, for example. The patterns908 and 910 are illustrated as darker sections of the bar graphs, withlines outlining a border of the bars falling within the pattern. In thismanner, the user interface of FIG. 9 highlights the detected patterns inthe charts 902, 904 and 906.

In some implementations, the bars in charts 902, 904, 906 may bedisplayed as sedimentary layers where each day's contributors arestacked on top of each other in a different color. In this way, a usercan visually discriminate the contributions to the bars by day.

Using Response Curves to Improve CGM

A response curve detection method may be performed in accordance withsome embodiments. Specifically, the detection of the start of asignificant event may be performed by monitoring a user's EGV over time.This may also include finding a plateau of the event to indicate whenthe event is over, and how to cope with some amount of fluctuation/noisethat would still be considered part of the event.

Similar to a rate of change calculation, a computer device, such as oneor more of devices 8, 14, 16, 18, 20 of FIG. 1 , may calculate thecurrent response curve. The computer device may have a response curvealert letting the user know that an event or response is taking too longto stabilize. The computer device may display the last few responsecurves, displaying start time, delta duration, delta EGV, and EGV rate.The patient may use the response curve results to make decisions abouthow to alter their medication, food intake, or behavior for the nextevent. For example, the curve may indicate to the user that certainbehaviors, e.g. exercise or caloric intake, trigger a particularresponse. As a result, the user may alter their behavior based on theindicated pattern information. In addition, the user may provide asinput details regarding the behavior which may be analyzed and displayedalongside the detected patterns. For example, the user may provide thenumber of calories consumed, the type of food consumed, and/or the typeof activity performed by the user at various time periods in a day.These inputs may be used for pattern analysis and indicate patterns inEGV levels to the user based on the user's behavior.

Further, the computer device may prompt the user to enter an eventwhenever the computing system detects a significant response curve. Forexample, following a period of exercise, the user's EGV levels may fallbelow a particular threshold or may exhibit a particular response curvewhich is detected by the computing system. The user may be prompted toenter the activity performed and time of day information in order toanalyze and detect pattern data.

The response curves (and entered event data) can be displayed on chartsand graphs to aid users in quickly identifying areas of responses thatneed better control. For example, the computing system could use theonset of response curves to automatically detect pre/post meal times andautomatically calculate pre/post meal statistics. The user may then makelong term decisions, rather than reactionary decisions, regardingcorrection of EGV levels based on the statistics and detected patterndata.

A computing system may maintain state information regarding patternanalysis process iterations. For example, the information may include alist of events that have been detected, including the start time and endtime of each event. Further, the state information may include thepatterns that were selected for display, including a list of events thatmake up the pattern. The state information may also include a patternanalysis log, which includes the time the analysis was performed, andthe time at which next analysis should start. For example, the time atwhich the next analysis should start may correspond to the start time ofan episode that did not end by the time the last iteration wasperformed.

Embodiments discussed herein may assist in training a user to makebetter decisions within a few hours of a prior problem. Rather thanconstantly requiring the user to enter event data, embodiments discussedherein may allow a user to focus on entering information about problemareas that need more attention as opposed to needing to enter a myriadof information. Further, embodiments discussed herein may use responsecurves to create a more accurate analysis of times of the day instead ofonly using fixed times throughout the day.

It is further believed that some embodiments can improve diabetesmanagement, improve trend detection, improve glucose control, reducehyperglycemic and hypoglycemic events, improve information for the userto take action upon in a more timely matter, and improve a patient'srecognition of patterns.

Applying response curves may also introduce retrospective informationover a longer time frame than rate of change information alone and maybe more quantitative than a trend line.

Further Implementations of User Interface for Displaying Results ofPattern Detection

As discussed herein, a user interface may be provided to allow a user toaccess and define parameters for pattern detection and analysis. Theuser interface can be incorporated into a graphical user interface ofone or more of devices 14, 16, 18, or 20 of FIG. 1 , for example.

In some implementations, the graphical user interface may enable a userto change the parameters for any of the pattern detection processesdescribed herein. The following is a non-exclusive list of exampleparameters that the user can change: threshold values (e.g. TH1, TH2,TP1) discussed with reference to FIG. 6 ; and bounds criteria, weightedassignment maps, pattern thresholds, and pattern occurrence criteria(discussed with reference to FIG. 7 ).

In some implementations, the graphical user interface may allow a userto initiate the pattern analysis at any point in time and select whenthe pattern analysis will be run.

In some implementations, the graphical user interface may allow a userto view a list of patterns that have been found so far. For example, thelist may display the last five patterns that have been found. Each itemin the list may show, for example, the following information: ID of thepattern (for example, each pattern may be coded with a pattern numberand may be displayed as Pnn, where nn corresponds to the ID number ofthe pattern); and the dates over which the pattern spans: For example:03/21/2011 to 04/05/2011; the time of day where the pattern was found(for example: 2:00 AM to 5:00 AM).

In some implementations, the graphical user interface may allow a userto access a view that displays the trend of the first day in the pattern(e.g. a 6 hour graph around the pattern). The user interface may alsoinclude user-selectable up and down buttons to scroll to the next andprevious day. This view of the interface may also have a windowdisplaying trend graphs from all days, overlapped.

When the pattern analysis method runs automatically and finds a pattern,the user interface my trigger a notification to the user. When the useracknowledges the notification, the list of patterns may be displayedwith the latest pattern highlighted. The user can then press select thehighlighted pattern and view further details about the selected pattern.The menu showing the list of patterns may also have a menu item totransition the device such that the trend screen is displayed to theuser.

The user interface can also include a menu for selecting a patternanalysis profile from a plurality of user selectable pattern analysisprofiles. Each pattern analysis profile can define the values of some orall of the thresholds and time periods used for pattern detection and/orweighted algorithms (e.g., weighted algorithm maps) depending upon thepattern analysis method being used. Each profile can also be associatedwith detection of particular pattern characteristics, such asidentifying a particular type of episode (e.g., hypoglycemic orhyperglycemic episode), a sensitivity associated with considering anevent an episode for pattern analysis, and time periods for analyzingdata (e.g., particular times of the data, such as morning, day, eveningor night). As an example, one exemplary profile can be associated withsettings that have a high sensitivity for determining hypoglycemicpatterns in the morning, and another exemplary profile can be associatedwith a low sensitivity for determining hypoglycemic events during theday. A profile associated with a high sensitivity for detectinghypoglycemic events may define some or all of the thresholds with highervalues and/or time periods between episodes with lower values than adifferent profile associated with a lower sensitivity so to be morelikely to consider an event a hypoglycemic episode and a plurality ofepisodes a pattern to alert a user.

In some implementations, a user can use the user interface to turn on oroff a hypoglycemia reoccurrence risk alert that, when turned on,triggers an alert if it is determined that there is a risk of the hostreoccurring into hypoglycemia within a predetermined time of havingalready detected a hypoglycemia event or episode. The hypoglycemic eventcan be characterized by any of the manners discussed herein or cansimply be characterized as the user's glucose concentration fallingbelow a threshold, such as 55 mg/dL. The predetermined amount of timecan be user configurable and can be 48 hours, for example. In someembodiments, the hypoglycemia risk alert can be triggered regardless asto whether the rate of change of the host's glucose concentration isfalling and regardless as to whether the user has another alert set totrigger an alarm at a low glucose concentration level. In someembodiments, the hypoglycemia reoccurrence risk alert includeshighlighting a portion of a trend graph displayed on the user interfacethat corresponds to the predetermined amount of time after the detectedsevere hypoglycemia event. Although not wishing to be bound by theory,it is believed that a person with diabetes is at risk for recurringhypoglycemia within 48 hours of a severe hypoglycemic event and, thus,the hypoglycemia reoccurrence risk alert can notify a user of this risk.

A graphical user interface 1100 is illustrated in FIG. 11 in accordancewith one implementation. The user interface 1100 can be implemented on adisplay of one or more of devices 14, 16, 18, or 20 of FIG. 1 , forexample. That is, user interface 1100 can be embodied as instructionsstored in computer memory and executed by one or more computerprocessors of device 14, 16, 18, or 20 to cause display of the graphicaluser interface on the display device.

As illustrated in FIG. 11 , graphical user interface 1100 includes aplurality of tabs 1102 a-1102 k. A user can select one of tabs 1102a-1102 k to cause user interface 1100 to switch to a view correspondingto the tab. The tabs 1102 a-1102 k of user interface 1100 include a hometab 1102 a, a pattern tab 1102 b, a hourly stats tab 1102 c, a dailytrends tab 1102 d, and distribution tab 1102 e, a glucose trend tab 1102f, a daily stats tab 1102 g, and success report tab 1102 h an A1crecords tab 1102 i, a patients tab 1102 j and an options tab 1102 k.User interface 1100 in FIG. 11 illustrates the view corresponding to thepatterns tab 1102. The patterns tab 1002 is selected in FIG. 11 .

User interface 1100 includes a pattern chart 1104. In thisimplementation, the pattern chart 1104 includes a modal day trend chart1110 section showing day over day estimated glucose value (EGV) trendlines, a high (above target) pattern plot chart 1112 section thathighlights significant pattern ranges (none shown in FIG. 11 ), and alow (below target) pattern plot chart 1114 section with highlightedsignificant pattern ranges 1116 a-1116 e, a pattern insights summarytable 1118 and a statistics table 1120. The model day trend chart 1110,high pattern plot chart 1112 and low pattern plot chart 1114 can beimplemented as discussed above with respect to FIG. 7 and FIG. 9 .Further, as illustrated in FIG. 11 , the model day trend chart 1110,high pattern plot chart 1112 and low pattern plot chart 1114 each havethe same x-axis scale, which can facilitate comparing the data shown ineach of the charts.

Pattern chart 1104 includes a title 1106 and a legend 1108 located atthe top of the chart. The title 1106 can be a textual descriptiondescribing a type or types of patterns to be detected in the patternplot chart 1112. The legend 1108 include markers associated with eachdifferent EGV trace in the model day chart 1110, where each marker canbe differentiated from the markers making up the other traces by colorand/or shape. Further, the legend 1108 can indicate the day and dateassociated with each of the markers.

Graphical user interface 1100 also includes a user selectable nighttimetime range button 1122. In some implementations, each patient can have aspecific range of time that indicates times of day when it is considerednighttime, such as 10 PM to 6 AM as illustrated in FIG. 11 , althoughany other range can be used instead. Graphical user interface 1100displays the currently defined nighttime time range for the patient asvertical light grey strip lines 1124 a and 1124 b that coincide with thestart and end of the times, respectively, of the nighttime range. Thestrip lines 1124 a and 1124 b can be shown on the model day chart 1110and both pattern charts 1112 and 1114.

Selecting the nighttime range button 1122 can initiate a time of dayrange editor window to be displayed on graphical user interface 1100.This editor window can allow the user to change the nighttime range.Changing to the patient's nighttime range can cause user interface 1100to automatically update to reflect the change in nighttime range, suchas changing the strip lines 1124 a and 1124 b to reflect the change andany insights related to the nighttime range reflected in pattern insightsummary 1118 and statistics summary 1120.

Graphical user interface 1100 can also include a display setting textbox 1126 that displays a timeframe (i.e. the range of dates and/or timesof patient data) presently being analyzed and displayed using the userinterface. In some implementations, text box 1126 can also include anindication if the patient data is blinded.

Graphical user interface 1100 also includes a user selectable targetglucose range button 1128. In some implementation, the target glucoserange defines a range of glucose values in which a patient desires tomaintain his or her glucose concentration. A glucose concentration abovethe target range can be considered a “high,” and a glucose concentrationbelow the target range can be considered a “low.” Graphical userinterface 1100 can display a high line 1130 a and a low line 1130 b onthe model chart 1110 representing the high of the target range and thelow of the target range, respectively. The sections of model chart 1110separated by the high line 1130 a and low line 1130 b can bedistinctively displayed from one another, such as each section having adifferent shading or color. In one implementation, the section of themodel chart 1110 above the high line 1130 a is a yellow color, thesection between lines 1130 a and 1130 b is a green color and the sectionbelow line 1130 b is a red color. These colors associated with high,target and low sections can also be used in other parts of graphicaluser interface 1100 that relate to respective high, target or lowglucose values. For example, the text “Night time Low” insight inpattern insight summary 1118 can be also displayed in a red colorbecause it relates to a low glucose value as defined by the targetglucose range.

Selecting the target glucose range button 1128 can cause a targetglucose range editor window to be displayed (e.g., a pop up window) ongraphical user interface 1100. This editor window can allow the user toselect of modify the target glucose range. Changing to the patient'starget glucose range can cause user interface 1100 to automaticallyupdate to reflect the change in target glucose range, such as changingthe lines 1130 a and 1130 b to reflect the target range change, anyinsights and statistics in pattern summary 1118 and statistics 1120determined based on how the target range is defined.

As discussed above, graphical user interface 1100 can indicate if theuser is in a blinded mode (e.g., user interface 1100 can display“Blinded” in the text box 1126). If the user is in a blinded mode, thengraphical user interface 1100 does not display certain information inaccordance with some implementations, such any view showing a user'sactual EGV values. In one implementation, when in the blinded mode,graphical user interface still displays the Sensor Usage,Calibrations/day, Target Range, and Nighttime Range in the Statisticstable 1120, but does not display one or more of the model chart 1110,pattern charts 112 and 1114, and Glucose Average in the statistics table1120.

The high and low pattern plot charts 1112 and 1114 can be implemented inany manner described above with respect to FIGS. 7-9 . To furtherdescribe use of high and low pattern plot charts 1112 and 1114, thefollowing illustrates some exemplary implementations.

The high and low pattern plot charts 1112 and 1114 can display a linegraph across all 288 of the 5 minute intervals of a day. Each 5 minuteinterval's plotted value can be the sum of all the EGV contributionsthat were above/below the target range limits in that interval and wherethe contribution amount is a weighted value as function of how far awayfrom the target limits the EGV value was. In one implementation, thefurther away from target, the larger the weighted contribution value isfor that EGV in that interval, but other weighted functions can be used,such as any of the weight maps or functions described above.

The high and low pattern plot charts 1112 and 1114 can be useful tohighlight significant time ranges where a set of intervals have exceededthresholds for both frequency (n out of m days) and severity (theaverage of weighted contributions). In some implementations, asignificant set of intervals is defined as having at least 3 intervals(15 minutes) and coalesce with an adjoining significant interval if lessthan 9 intervals (45 minutes) away. An interval can also be defined inany of the manners discussed herein with reference to defining a segmentor pattern.

FIG. 11 illustrates significant time ranges 1116 a-1116 e in the Lowpattern chart 1114. No significant time ranges were identified in theHigh pattern chart 1112. Accordingly, a user can discern that they maywant to try to modify his or her behavior to better hypoglycemiccontrol.

In some implementations, the detection of significant time ranges on thehigh and low pattern plot charts 1112 and 1114 have increasedsensitivity (i.e. a lower threshold on the average of weightedcontributions) during the nighttime range, which can be defined usingnighttime range button 1122, as discussed above.

In some implementations of user interface 1100, the detection ofsignificant time ranges on the high and low pattern plot charts 1112 and1114 is automatically disabled if the current number of days viewed isonly one or two days. That is, in some implementations, at least threepossible days of contributing EGV values are needed for the frequencythreshold to be considered.

In some implementations, the y-axis scale of the high and low patternplot charts 1112 and 1114 can be adaptive and relative to the timeframeof data chosen to be analyzed and displayed. The scale can be adjustedto be approximately 80% of the maximum possible sum of weighted glucosecontribution values.

The pattern summary table 1118 of user interface 1100 can indicate atotal number of significant patterns and a description (e.g., timerange) of one or more of the most significant patterns. The patterns canbe grouped by four categories: Nighttime Lows 1132 a, Daytime Lows 1132b, Nighttime Highs 1132 c, and Daytime Highs 1132 d. The mostsignificant pattern in each of the four groups can be determined by avolume; that is, a total sum of all contributed values within thepattern interval. A pattern can be considered in a nighttime group ifthe patterns start time or nadir point is in the patient's definednighttime range, and, conversely, a pattern can be considered in adaytime group if the pattern's start time or nadir point is in thepatient's defined nighttime range.

In some implementations, when hovering a computer cursor (e.g., thepointer controlled by a computer mouse) or finger, in the case of userinterface 1100 having touch-sensitive screen capabilities, over one ofthe rows 1132 a-1132 d of the pattern summary table 1118, the userinterface 1100 can highlight the time interval corresponding to theselected pattern using, for example, a shaded vertical strip extendingacross each of the charts 1104, 1112, and 1114 (not shown).

The statistics table 1120 can display a variety of statistical valuesbased on data falling within the selected timeframe being viewed on userinterface 1100. The statistics table 1120 can include: a glucose average1134 a (i.e. the mean of all glucose values found in the currentlyselected days being viewed); sensor usage 1134 b (e.g. determined basedon the number of days that contain at least one glucose value out of thenumber of days currently selected); calibrations per day 1134 c (e.g.,determined based on the total number of meter values found in thecurrently selected days divided by the sensor usage days); standarddeviation 1134 d (e.g., the standard deviation based on all of theglucose values found in the currently selected days being viewed);percentage of high values 1134 e (e.g., determined based on thepercentage of glucose values above the patient's defined target range);percentage of low values 1134 f (e.g., determined based on thepercentage of glucose values below the patient's defined target rangefound in the currently selected days being viewed); percentage target1134 g (e.g., determined based on the percentage of glucose valueswithin (or equal to) the patient's defined target range found in thecurrently selected days being viewed); High/Low/Target pie chart 1134 hrepresentative of the percentage of high, low and target values; thecurrently defined patient's target glucose range 1134 i (defined usingtarget glucose range button 1128, for example); and the currentlydefined patient's nighttime range 1134 j (defined using nighttime rangebutton 1122, for example).

Graphical user interface 1100 also allows a user to select the timeframeof data to be analyzed and displayed on graphical user interface 1100.In some implementations, a user can select a timeframe in the range of 1to 15 days, 1 to 18-28 days, 1 to 30 days, 1 to 60 days and 1 to 90days.

To facilitate user selection and modification of the timeframe, the userinterface 1100 includes a date slider control 1136. The date slidercontrol 1136 can be used for choosing the timeframe of displayedinformation in any of the different views associated with tabs 1102a-1102 j.

In implementation of the date slider control 1136 is described furtherwith respect to FIG. 12 . Date slider control 1136 includes auser-selectable size control button 1202 to choose the number of days todisplay (i.e. the number of days in the timeframe). A user can selectthis section of the slider 1136 to cause a drop down menu to bedisplayed for a user to select a number of days desired to be used inthe timeframe. The size control button 1202 can also display the currentnumber of days in the current timeframe. In some implementations, thenumber of days is always “whole” days from 12:00 am to midnight.

Date slider control 1136 also includes start date button 1204. When auser selects the start date button 1204, the timeframe jumps to thefirst date of available data to be used with user interface 1100 (e.g.,first date of data stored on device 16, 16, 18 or 20 implementing userinterface 1100). Further, slider control 1136 can indicate to the userthe first date of available data by hovering over the start date button1204, which causes display of a tool tip 1206 popup that indicates thedate of the first data available.

Date slider control 1136 also includes end date button 1208. When a userselects the end date button 1208, the timeframe jumps to the last dateof available data to be used with user interface 1100 (e.g., last dateof data stored on device 16, 16, 18 or 20 implementing user interface1100). Further, slider control 1136 can indicate to the user the lastdate of available data by hovering over the end date button 1208, whichdisplays a tool tip 1210 popup that indicates the date of the last dataavailable.

Date slider control 1136 includes a slider bar 1212 that a user can draghorizontally to modify the date selection timeframe. Dragging the sliderbar 1212 in a right-hand direction (i.e. toward the start date button1204) causes the timeframe to increment back in time from a current dateselection, and dragging the slider bar in a left-hand direction (i.e.toward the end date button 1208) causes the timeframe to incrementforward in time from a current date selection. Slider bar 1212 alsodisplays the currently selected date range (start to end, inclusive) ofthe timeframe. To illustrate, a currently selected time frame can beseven dates spanning from June 10 to June 16. Sliding the bar 1121 in aleft-hand direction causes the timeframe to increment back in time, suchas to June 3-June 9. Note that in this implementation, the number ofdays in a timeframe remains constant unless the view size 1202 ismodified. Thus, the number of days in the timeframe remains the same asthe slider is dragged.

Date slider control 1136 includes day earlier button 1214 and day laterbutton 1216, which increments the timeframe one day forward or one daylater, respectively, when selected.

Date slider control 1136 can also allow a user to increment n number ofdays forward or backward by selecting portions 1218 and 1220 of slidercontrol 1136 located before and after the slider bar 1212, respectively.The number n can correspond to the number of days selected in sizecontrol button 1202. To illustrate, if the number of days selected inthe size control 1202 is seven days, then selecting portion 1218 ofslider control 1136 causes the slider bar 1212 to slide toward the startdate button 1204 and cause the timeframe (both start and end date) toincrement back in time seven days.

In accordance with some embodiments, a user can use a device (e.g.,display device 14, 16, 18, 20) to implement one or more of the patterndetection techniques described herein to identify and display patternsuseful for a user to monitor so to manage a chronic disease, such asdiabetes. For example, the pattern analysis methods described herein canbe tailored to detect hyperglycemic and/or hypoglycemic patternsoccurring at particular times of the day or on particular days of theweek. Patterns can be scored or weighted to identify patterns based onfrequency, sequence and severity, including time spent in severeepisodes. Alerts can also be triggered based on previous patternsanalysis and current data indicating that the current data is followinga similar enough pattern as the past identified patterns. Devicesdisclosed herein can also be used to input a comment regarding what auser did in response to an alert triggered in response to detection of apattern.

While the disclosure has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or exemplary and not restrictive. Thedisclosure is not limited to the disclosed embodiments. Variations tothe disclosed embodiments can be understood and effected by thoseskilled in the art in practicing the claimed disclosure, from a study ofthe drawings, the disclosure and the appended claims.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’‘including but not limited to,’ or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ ‘containing,’ or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit, and each intervening value between the upper and lowerlimit of the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

It will be appreciated that, for clarity purposes, the above descriptionhas described embodiments with reference to different functional units.However, it will be apparent that any suitable distribution offunctionality between different functional units may be used withoutdetracting from the invention. For example, functionality illustrated tobe performed by separate computing devices may be performed by the samecomputing device. Likewise, functionality illustrated to be performed bya single computing device may be distributed amongst several computingdevices. Hence, references to specific functional units are only to beseen as references to suitable means for providing the describedfunctionality, rather than indicative of a strict logical or physicalstructure or organization.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. Therefore, the descriptionand examples should not be construed as limiting the scope of theinvention to the specific embodiments and examples described herein, butrather to also cover all modification and alternatives coming with thetrue scope and spirit of the invention.

What is claimed is:
 1. A method for alerting a user based onmeasurements of an analyte concentration, the method comprising:receiving the measurements of the analyte concentration of a host froman analyte sensor system; detecting a hypoglycemic event based on atleast one of the measurements exceeding at least one predeterminedthreshold; determining, based on the detected hypoglycemic event, ahypoglycemic reoccurrence risk associated with the host experiencing asecond hypoglycemic event within a predetermined amount of timefollowing the detected hypoglycemic event; and displaying a trend graphof the measurements over time on a user interface, wherein the trendgraph includes an indication of the hypoglycemic reoccurrence riskassociated with the predetermined amount of time following the detectedhypoglycemic event.
 2. The method of claim 1, wherein detecting thehypoglycemic event comprises determining a segment of time the measuredanalyte concentration is below a predetermined analyte concentrationlevel.
 3. The method of claim 1, further comprising: receiving a userselection to turn on a setting for displaying hypoglycemic reoccurrencerisks, wherein the displaying of the trend graph is based on receivingthe user selection.
 4. The method of claim 1, wherein the predeterminedamount of time is 48 hours.
 5. The method of claim 1, further comprisingtriggering an audible or visual alert using the user interfaceresponsive to detecting the hypoglycemic event and detecting that acurrent rate of change of the analyte concentration exceeds apredetermined threshold.
 6. The method of claim 1, further comprisingtriggering an audible or visual alert using the user interfaceresponsive to detecting the hypoglycemic event.
 7. An analyte monitoringsystem, comprising: an analyte sensor system configured to generatemeasurements of an analyte concentration; at least one memory comprisingexecutable instructions; and at least one processor in datacommunication with the memory and configured to execute the instructionsto cause the analyte monitoring system to: receive the measurements ofthe analyte concentration of a host from the analyte sensor system;detect a hypoglycemic event based on at least one of the measurementsexceeding at least one predetermined threshold; determine, based on thedetected hypoglycemic event, a hypoglycemic reoccurrence risk associatedwith the host experiencing a second hypoglycemic event within apredetermined amount of time following the detected hypoglycemic event;and display a trend graph of the measurements over time on a userinterface, wherein the trend graph includes an indication of thehypoglycemic reoccurrence risk associated with the predetermined amountof time following the detected hypoglycemic event.
 8. The analytemonitoring system of claim 7, wherein detecting the hypoglycemic eventcomprises determining a segment of time the measured analyteconcentration is below a predetermined analyte concentration level. 9.The analyte monitoring system of claim 7, wherein the at least oneprocessor is further configured to: receive a user selection to turn ona setting for displaying hypoglycemic reoccurrence risks, wherein thedisplaying of the trend graph is based on receiving the user selection.10. The analyte monitoring system of claim 7, wherein the predeterminedamount of time is 48 hours.
 11. The analyte monitoring system of claim7, wherein the at least one processor is further configured to: triggeran audible or visual alert using the user interface responsive todetecting the hypoglycemic event and detecting that a current rate ofchange of the analyte concentration exceeds a predetermined threshold.12. The analyte monitoring system of claim 7, wherein the at least oneprocessor is further configured to: trigger an audible or visual alertusing the user interface responsive to detecting the hypoglycemic event.13. A non-transitory computer readable medium having instructions storedthereon that, when executed by at least one processor, cause the atleast one processor to perform a method for alerting a user based onmeasurements of an analyte concentration, the method comprising:receiving the measurements of the analyte concentration of a host froman analyte sensor system; detecting a hypoglycemic event based on atleast one of the measurements exceeding at least one predeterminedthreshold; determining, based on the detected hypoglycemic event, ahypoglycemic reoccurrence risk associated with the host experiencing asecond hypoglycemic event within a predetermined amount of timefollowing the detected hypoglycemic event; and displaying a trend graphof the measurements over time on a user interface, wherein the trendgraph includes an indication of the hypoglycemic reoccurrence riskassociated with the predetermined amount of time following the detectedhypoglycemic event.
 14. The non-transitory computer readable medium ofclaim 13, wherein detecting the hypoglycemic event comprises determininga segment of time the measured analyte concentration is below apredetermined analyte concentration level.
 15. The non-transitorycomputer readable medium of claim 13, wherein the method furthercomprises: receiving a user selection to turn on a setting fordisplaying hypoglycemic reoccurrence risks, wherein the displaying ofthe trend graph is based on receiving the user selection.
 16. Thenon-transitory computer readable medium of claim 13, wherein thepredetermined amount of time is 48 hours.
 17. The non-transitorycomputer readable medium of claim 13, wherein the method furthercomprises: triggering an audible or visual alert using the userinterface responsive to detecting the hypoglycemic event and detectingthat a current rate of change of the analyte concentration exceeds apredetermined threshold.
 18. The non-transitory computer readable mediumof claim 13, wherein the method further comprises: triggering an audibleor visual alert using the user interface responsive to detecting thehypoglycemic event.